Keywords Spatial Data Mining, Association Rule Mining, Apriori Algorithm, Wastelands, Ground Water.
|
|
- Isabella Carson
- 5 years ago
- Views:
Transcription
1 Volume 5, Issue 6, June 2015 ISSN: X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: Association Rule Mining for Ground water and Wastelands Using Apriori Algorithm: Case Study of Jodhpur District Mainaz Faridi * Seema Verma Saurabh Mukherjee Department of Computer Science Department of Electronics Department of Computer Science Banasthali University, India Banasthali University, India Banasthali University, India Abstract The advancement and improvement in data collection and storage techniques have led to collect and store terabytes of data on daily basis. This large volume of data hides meaningful and interesting information that need to be brought in light. This has made data mining as one of the profoundly researched domain of the recent years. Uncovering and finding out the non- trivial, previously unknown and hidden information from large data repositories and data warehouses is the primary goal of data mining. Data mining when applied to spatial data sets is called Spatial Data Mining or Geographic Data Mining, where it can be used to characterize spatial data, interrelate spatial and non spatial data and depict hidden and veiled spatial patterns. Data mining has many methods for discovering the previously unseen patterns and trends such as clustering, classification, prediction, regression, outlier detection, association rule mining etc. In this research paper, authors propose to mine association rules between ground water and wastelands using spatial data mining techniques. The salt-affected waste lands and waste lands without scrubs showing higher ground water level underneath can be irrigated using this water thereby increasing the area under cultivation. Keywords Spatial Data Mining, Association Rule Mining, Apriori Algorithm, Wastelands, Ground Water. I. INTRODUCTION WRIS and BOOSAMPDA are two major projects run by ISRO (Indian Space Research Organization) and NRSC (National Remote Sensing Centre) providing country wide information on ground water and data relevant to land cover across India in form of maps respectively, producing huge amount of data related to ground water and land-cover[1]. The tremendous volume of numeric and geospatial data stored in different formats, databases and data repositories imposes a need for a wide range of tools and techniques to analyze, query, uncover data patterns or even predict phenomenon where human intelligence alone is not sufficient to solve complex cases [2] New technologies and methods are needed to explore these large databases for hidden and implicit knowledge, special patterns, or correlation between spatial and non spatial attributes[3]. Recent research activities on knowledge discovery on large spatial databases have paved a foundation for spatial data mining techniques. A. Spatial data mining Spatial data mining i.e. discovery of interesting, implicit knowledge in spatial databases, provides means for understanding and use of spatial data- and knowledge- bases. Spatial data mining is also referred to as Geographical Data Mining [4] and Knowledge Discovery in Spatial Database [5]. The main difference between data mining and spatial data mining is that in spatial data mining tasks we use not only non-spatial attributes (as it is usual in data mining in nonspatial data), but also spatial attributes. Traditional data mining has no or very little dependence between the studied variables and lacks the ability to correlate non-spatial attributes with spatial information [6]. Spatial data mining is the process to find and uncover useful and interesting patterns which are hidden in large spatial datasets. Revealing interesting and potentially useful patterns from large spatial datasets is much more complex than extracting the corresponding patterns from conventional numeric and categorical data sets. The complexity of spatial data types, relationships and autocorrelation of spatial attributes account to this difficulty [7]. B. Association Rule Mining using Apriori Algorithm Association Rule Mining (ARM) is an important and widely used technique of data mining. This is one of the extensively used and studied methods of data mining, having a wide range of application areas. The most common example is the market basket analysis where association between different consumer products is figured out which can assist in taking effective business and marketing decisions. Other application domains which provide large data sets where ARM can be applied are finance, insurance, banking, fraud detection, medical, bioinformatics, demographic studies, telecommunication, GIS, remote sensing, e-commerce and retailing. More recently association rule mining is also applied to areas like pharmaceutics, law and justice, aviation management, agriculture, weather forecast etc. Let there are T transactions in database D and X and Y are disjoint itemsets containing collection of items i.e. there intersection is null, (X Y = ). An association rule can be written in form X Y, where X is the antecedent (left hand side of the rule) and Y is the consequent (right hand side). A rule may contain more than one item in antecedent and consequent of rule. The strength and reliability of an association rule is measured by two factors: support and confidence. 2015, IJARCSSE All Rights Reserved Page 751
2 Support (prevalence) is percentage of database transactions that contains X and Y or it can be viewed as the probability where X and Y occur together i.e. σ (X Y). Support s for rule (X Y) can be calculated as: Support(s) for (X Y) = σ X Y (1) N Confidence (predictability) is the percentage of database transactions containing X that also contain Y. In other words, it could be seen as the conditional probability, σ(y X). It can be calculated as: Confidence(c) for (X Y) = σ(x Y ) (2) σ(x) Support provides statistical significance to the rule. If it is too low then it may be possible that the rule has occurred mere by chance. On the other hand, confidence measures reliability or predictability of the rule. If it is kept high then one can easily infer that Y is also present in transactions containing X. Therefore, to select only those rules which have high interestingness threshold levels are set on support and confidence values, called as minsup and minconf, respectively. Generally a low minsup and a high minconf are set to ensure that all the possible interesting rules have been mined. Association rules are mined in two phases. In first step (Frequent itemset generation), using minsup all the itemsets are found whose support is greater than minsup. Such itemsets are called frequent itemsets. In the next phase, all the rules are pruned from frequent itemsets, who satisfy the minconf threshold (Rule generation) [8]. 1)Apriori Algorithm: Many algorithms have been proposed for association rule mining. But the eminent one remains the Apriori Algorithm, proposed by Agrawal et. al in 1994 [9]. This has remained the much studied and researched algorithm even after many years of its introduction. Many advancements and extensions have been proposed for this algorithm, but its applicability to many areas has still to be utilized. Apriori algorithm works on the principle of downward closure property or anti monotone property. In order to generate frequent itemsets by searching all the possible itemsets, whole database needs to be scanned. To reduce the number of candidate itemsets during frequent itemset generation, anti monotone property is used. It states that if an itemset is frequent then all its subsets will also be frequent or if an itemset is not frequent then its supersets are also not frequent. Let P be the power set and X be the subset of Y. Reference [8] shows that a measure f is anti monotone if X, Y P: (X Y) f(y) f(x). Apriori algorithm uses breadth-first technique to search the candidate itemsets. It uses itemsets with k-1 length to generate itemsets of k length (join step). Then it uses the anti monotone property to generate frequent itemsets (prune step). Association rules can be generated by using frequent itemsets such that X Y-X. Those rules whose confidence does not satisfy minconf threshold are dropped out and only the remaining strong rules are chosen. 2)Pseudocode: The pseudo code for the algorithm is stated as follows: ALGORITHM. Apriori Input: D, a database of transactions; minsup, the minimum support count threshold. Output: L k, frequent itemsets in D. L1= {frequent 1-itemsets}; for(k= 2; L k-1!= ; k++) { C k = candidates generated from L k-1 //that iscartesian product L k-1 x L k-1 and eliminating any k-1 size itemset that //is not frequent for each transaction t in database do{ #increment the count of all candidates in C k that are contained in t L k = candidates in C k with minsup }//end for each }//end for return k L k ; } II. AIM AND OBJECTIVES Land and water are undoubtedly the two major natural resources which are essential for the very existence of life. With the increase of population the demand for land has raised many folds. Therefore, objective of the study is to find those barren lands having a substantial ground water level, so that these lands can be used for cultivation of crops and fodder for animals. The study aims to unearth association rules between ground water and wastelands of Jodhpur District. The outcomes will reveal some useful patterns helping us to relate ground water and wastelands. III. RESEARCH METHODOLOGY A.Study Area Jodhpur district comes under arid zone of the Rajasthan situated between & North latitude and & East longitude. It covers 11.60% of total arid area of the state. Jodhpur district, part of Jodhpur Division covers a geographical area of hectares and is divided into 5 sub-divisions that are Jodhpur, Shergarh, Pipar City, Osian & Phalodi. The district has 07 tehsils & 09 blocks. The district is bounded by Bikaner in North, Nagaur in East, Jaisalmer in west, and Barmer and Pali in the South. 2015, IJARCSSE All Rights Reserved Page 752
3 Fig.1. Map showing study area location B.Data Collection The study required information about land use, ground water and soil in the study area in GIS format. For the proposed system the data has been collected from Indian Space and Research organization (ISRO) Jodhpur Center. The center provided the data for land use, ground water and soil for Jodhpur district for the year 2005 in GIS format. The different types of dataset and their basic characteristics pertaining to this study are briefly described as follows: 1) Landuse Data of Jodhpur District: Land use Map of Jodhpur shows the division of land into Agricultural Land, Built-up, Forest, Waste-land, Water bodies and Wetlands. 2)Ground Water Data of Jodhpur District: Jodhpur District is classified into different regions depending upon on the level and quality of ground water viz. Good, Good but saline, Good to Moderate, Moderate, Moderate to Poor, Poor, poor to Nil, Saline, Settlement, Very Good to Good and Water Body mask. C. Tools/ Softwares used ArcMap 10 is used for creating thematic maps and overlays. Weka 3.6 is used for generating Association rules. D. Methods The methodology developed for this study is shown below in figure 2. Each block represents the sub-processing step to reach up to the final output. Fig. 2. Overall approach of the study. 1)Pre-processing of Data: The spatial datasets are preprocessed to create a transactional database before association rule mining can be applied. The preprocessing of spatial data may include selection of non spatial attributes, feature selection, dimension reduction, carrying out join, union or intersection operations, data categorization etc [10].The study required two different types of data set for ground water and waste lands. The pre-processing of data was carried in three steps: 2015, IJARCSSE All Rights Reserved Page 753
4 a.thematic layer with the required attributes is created for waste land data. b.thematic layer with the required attributes is created for ground water data. c.intersection is performed on the waste land and ground water layers to get a new intersection layer and a new thematic layer is created that shows those areas of Jodhpur district which are either salt-affected waste lands or waste lands without scrubs having ground water beneath. The details of the above pre-processing steps are as follows: a. Thematic Layers for Waste Land Land use data of Jodhpur district as provided by the ISRO center Jodhpur, classifies the land use into following types: Agricultural Land, Built-up, Forest, Waste-land, Water bodies and Wetlands. The table I shows the land use pattern in the order of decreasing area and figure 3 shows the land use map. Table I: Land Use Pattern Fig. 3. Land Use map of Jodhpur District Land Type Area(Hectares) Agriculture Waste-lands Built-up Water bodies Forest Wetlands Out of all the above classified lands, the study focuses on waste-lands only. Therefore, to get the waste-land distribution pattern a new thematic layer is prepared showing only waste lands. The figure 4 and table II show the newly created thematic layer for waste land only. The layer shows that the waste lands are again classified into Sandy-desertic Land, Salt Affected, Land Mining/ Industrial waste, Land without scrub, Land with scrub, Gullied/Ravenous Land, Barren Rocky/ Stony waste land. Table II: Waste Land Pattern Fig. 4. Waste Land distribution of Jodhpur District Waste-land Type Area(Hectares) Sandy-desertic Land Land without scrub Land with scrub Barren Rocky/Stony waste Mining Industrial waste Salt Affected Land Gullied/Ravenous Land Among all the types of waste lands only waste lands that are either salt affected or without scrubs are chosen for further study. The reason behind it is that all other types of waste-lands are either already contain some vegetation(land with scrub) or are not suitable for growing any type of vegetation(sandy-desertic Land, Land Mining/ Industrial waste, Gullied/Ravenous Land, Barren Rocky/ Stony waste land). Therefore, a new thematic layer for Land Without Scrubs and Salt Affected Waste Land is created. The figure 5 and table III show this layer. Table III: Waste Land (Salt affected/ Without Scrub) Wasteland Land without scrub Salt Affected Land Area(Hectares) Fig. 5. Waste Land (Salt affected/without Scrub) distribution. 2015, IJARCSSE All Rights Reserved Page 754
5 Thus, the above process can be summarized as: Fig. 6. Thematic layers of Land use data b. Thematic Layers for Ground water Ground water data, as provided by the ISRO Center, Jodhpur is classified into different types like Good, Good but saline, Good to Moderate, Moderate, Moderate to Poor, Poor, poor to Nil, Saline, Settlement, Very Good to Good and Water Body mask. Based on this classification Jodhpur District is divided into these regions.this distribution of ground water is shown in the figure 7 and table IV. Table IV: Ground water Pattern Fig. 7. Ground water distribution of Jodhpur. Ground Water Area (Hectares) Good Good but Saline Good to moderate Moderate Moderate to Poor Poor Poor to Nil Saline Settlement Very to Water Body Mask Out of these classified regions, only those regions of Jodhpur District are selected having Good, Good but saline, Good to Moderate and Very Good to Good ground water level. As a next step, new thematic layer for ground water is created containing only the selected attributes as showed in figure 8 and table V. Table V: Good ground water Pattern Ground Water Area(Hectare Good s) Good but Saline Good to moderate Very to Figure 8: Good ground water distribution of Jodhpur District. 2015, IJARCSSE All Rights Reserved Page 755
6 Thus, the above process can be summarized as shown in figure 9: Fig. 9. Thematic layers of Ground water data. c. Overlays and Intersection of Thematic Layers As the next step overlay maps of waste lands (salt affected and without scrubs) and ground water is created. An overlay operation is much more than a simple merging of linework, all the attributes of the features taking part in the overlay are carried through, as shown in the figure 10 below, where wastelands (polygons) and ground water (polygons) are overlayed to create a new polygon layer. Fig. 10. Overlay Map of Wasteland (Salt affected/ Without Scrub) and Good Ground Water. Then a new layer is created for those areas of the district having waste lands which are salt affected or without scrub and have ground water beneath, by using intersection. The newly constructed layer is shown in the figure 11. Table VI shows the area under mining pattern. Table VI: Area under mining pattern. Waste Land Area(Hectare s) Land without scrub Salt Affected Land Total Fig.11. Intersect Map of Wastelands (Salt affected/without Scrub) and Good Ground Water. 2)Association Rules Generation: For generating Association rules, a tool called Weka 3.6 is used. The database file obtained from the above map (figure 11) is converted into ARFF format on which association rules are generated using Apriori algorithm. IV. RESULTS AND DISCUSSION Apriori algorithm was run in Weka using the arff file created after the preprocessing of data. Three attributes were chosen viz. Taluk, WasteLandType and GroundWaterType from the database file as predicates. Six itemsets of size1, 7 itemsets of size 2 and 2 itemsets of size 3 were discovered from a total of 285 instances of data in 17 cycles. Minimum support and minimum confidence kept were 15% (0.15) and 90% (.9) respectively. Tables VII,VIII and IX show large item sets found in the data. 2015, IJARCSSE All Rights Reserved Page 756
7 Table VII. Large Itemsets L(1) Item 1 Count Taluk=Bilara 99 Taluk=Jodhpur 76 Taluk=Phalodi 61 WasteLandType=Landwithout scrub 280 GroundWaterType=Very to 181 GroundWaterType=Good 44 Table VIII. Large Itemsets L(2) Item 1 Item 2 Count Taluk=Bilara WasteLandType=Land without scrub 99 Taluk=Bilara GroundWaterType=Very to 81 Taluk=Jodhpur WasteLandType=Land without scrub 76 Taluk=Jodhpur GroundWaterType=Very goog to 75 Taluk=Phalodi WasteLandType=Land without scrub 57 WasteLandType=Land without scrub GroundWaterType=Very to 181 WasteLandType=Land withut scrub GroundWaterType=Good 44 Table IX. Large Itemsets L(3) Item 1 Item 2 Item 3 Count Taluk=Bilara WasteLandType=Land without GroundWaterType=Very to scrub 81 Taluk=Jodhpur WasteLandType=Land without GroundWaterType=Very to scrub 75 The best rules found after applying Apriori algorithm are listed in the table X below. Table X. Association Rules Mined for Ground Water and Waste Lands of Jodhpur District. S.No. Body Implies Head Support Conf % 1. GroundWaterType=Very to ==> WasteLandType=Land without scrub Taluk=Bilara ==> WasteLandType=Land without scrub Taluk=Bilara ==> WasteLandType=Land without scrub GroundWaterType=Very to Taluk=Jodhpur 76 ==> WasteLandType=Land without scrub Taluk=Jodhpur ==> WasteLandType=Land without scrub GroundWaterType=Very to GroundWaterType=Good 44 ==> WasteLandType=Land without scrub Taluk=Jodhpur 76 ==> GroundWaterType=Very to Taluk=Jodhpur ==> GroundWaterType=Very to WasteLandType=Land without scrub Taluk=Jodhpur 76 ==> WasteLandType=Land without scrub GroundWaterType=Very to 10. Taluk=Phalodi 61 ==> WasteLandType=Land without scrub Results show that hectares of land fall under mining pattern. Analysis of results is shown in form of a graph in figure 12. It shows that Bilara has the maximum ( hectares) waste lands distribution of the mined pattern. The area mined is substantially a large one that can be utilized for vegetation production using the water underneath. The same results presented above are obtained by implementing the WEKA Apriori Algorithm in own Java code. 2015, IJARCSSE All Rights Reserved Page 757
8 Fig.12. Graph showing distribution of Wastelands in taluks of Jodhpur District. V. CONCLUSION The analysis of pattern shows that majority of wastelands without scrubs having very high groundwater lie in Bilara region of Jodhpur District. Having amount of water underneath, these lands can be used to produce firewood and fodder for animals. Plant species like Acacia jacquemontii, Acacia leucophloea, Acacia senegal, Albizia lebbeck, Azadirachta indica, Anogeissus rotundifolia, Prosopis cineraria, Salvadora oleoides, Tecomella undulata, Tamarix articulate, Leucaena leucocephala, Tephrosia purpurea and Crotalaria medicaginea can be grown. Farmers can be advised to cultivate crops using ground water irrigation. If we know that a land has ground water level, then land can be irrigated using this water. Even if the water underneath is saline, then also salt resistant species of plants can be grown. In this way we can effectively utilize waste-lands. VI. FUTURE WORK A wide variety of research is being carried in the field of spatial data mining. As the next level of this research, Fuzzy Spatial Association Rules could be determined. Soil and crop data could also be used along with the ground water and wasteland data. Also spatio-temporal association rules could be determined as an extension to this current research. Hence, a lot of research is needed to be carried out in these emerging areas, focusing on its applicability to agriculture, data mining and GIS, which will provide means for better utilization of natural resources. ACKNOWLEDGMENT The authors would like to thank ISRO, Jodhpur Centre for providing necessary data about the research scenario. REFERENCES [1] Mainaz Faridi, Seema Verma and Saurabh Mukherjee Impact of ground water level and its quality on fertility of land using GIS and Agriculture Business Intelligence. In Proceedings of Geomatrix 12- An International Conference on Geospatial Technologies and Applications, IIT Bombay (Feb 2012). [2] Yuan, May, B. Buttenfield, M. Gahegan, and Harvey Miller Geospatial data mining and knowledge discovery. Chapter 14 (2004): [3] Krzysztof Koperski, and Jiawei Han Discovery of spatial association rules in geographic information databases. Advances in spatial databases, Springer Berlin Heidelberg. vol 6, [4] Stan Openshaw Geographical data mining: key design issues. In Proceedings of GeoComputation, vol. 99. [5] Krzysztof Koperski, Jiawei Han, and Nebojsa Stefanovic An efficient two-step method for classification of spatial data. In Proceedings of International Symposium on Spatial Data Handling (SDH 1998), Vancouver, BC, Canada [6] Hong Tang and Simon McDonald Integrating GIS and spatial data mining technique for target marketing of university courses. In ISPRS Commission IV, Symposium, Ottawa Canada, (Jul 2002). [7] D. Rajesh Application of Spatial Data Mining for Agriculture. International Journal of Computer Applications 15,2 (2011), 7-9. [8] Tan, Pang-Ning, and Vipin Kumar Chapter 6. Association Analysis: Basic Concepts and Algorithms." Introduction to Data Mining. Addison-Wesley. ISBN (2005). [9] Rakesh Agrawal, and Ramakrishnan Srikant Fast algorithms for mining association rules. In Proceedings of 20th int. conf. very large data bases, VLDB, (1994), vol. 1215, [10] Chen, Junming, Guangfa Lin, and Zhihai Yang Extracting spatial association rules from the maximum frequent itemsets based on Boolean matrix. In Geoinformatics, th International Conference on, IEEE (2011), , IJARCSSE All Rights Reserved Page 758
CSE 5243 INTRO. TO DATA MINING
CSE 5243 INTRO. TO DATA MINING Mining Frequent Patterns and Associations: Basic Concepts (Chapter 6) Huan Sun, CSE@The Ohio State University Slides adapted from Prof. Jiawei Han @UIUC, Prof. Srinivasan
More informationData Mining: Concepts and Techniques. (3 rd ed.) Chapter 6
Data Mining: Concepts and Techniques (3 rd ed.) Chapter 6 Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University 2013 Han, Kamber & Pei. All rights
More informationChapter 6. Frequent Pattern Mining: Concepts and Apriori. Meng Jiang CSE 40647/60647 Data Science Fall 2017 Introduction to Data Mining
Chapter 6. Frequent Pattern Mining: Concepts and Apriori Meng Jiang CSE 40647/60647 Data Science Fall 2017 Introduction to Data Mining Pattern Discovery: Definition What are patterns? Patterns: A set of
More informationCSE 5243 INTRO. TO DATA MINING
CSE 5243 INTRO. TO DATA MINING Mining Frequent Patterns and Associations: Basic Concepts (Chapter 6) Huan Sun, CSE@The Ohio State University 10/17/2017 Slides adapted from Prof. Jiawei Han @UIUC, Prof.
More informationDEVELOPMENT OF KNOWLEDGE MINING TECHNIQUES FOR SPATIAL DECISION SUPPORT SYSTEM
International Journal of Computer Engineering & Technology (IJCET) Volume 8, Issue 6, Nov-Dec 2017, pp. 95 105, Article ID: IJCET_08_06_012 Available online at http://www.iaeme.com/ijcet/issues.asp?jtype=ijcet&vtype=8&itype=6
More informationData Analytics Beyond OLAP. Prof. Yanlei Diao
Data Analytics Beyond OLAP Prof. Yanlei Diao OPERATIONAL DBs DB 1 DB 2 DB 3 EXTRACT TRANSFORM LOAD (ETL) METADATA STORE DATA WAREHOUSE SUPPORTS OLAP DATA MINING INTERACTIVE DATA EXPLORATION Overview of
More informationAssocia'on Rule Mining
Associa'on Rule Mining Debapriyo Majumdar Data Mining Fall 2014 Indian Statistical Institute Kolkata August 4 and 7, 2014 1 Market Basket Analysis Scenario: customers shopping at a supermarket Transaction
More informationAnalyzing the Factors of Deforestation using Association Rule Mining
Analyzing the Factors of Deforestation using Association Rule Mining Mrs. K.R.Manjula Associate Professor, Dept. of MCA, SIET, Puttur manju_sakvarma@yahoo.co.in Mr.S.Anand Kumar Varma Associate Professor,
More informationGeovisualization for Association Rule Mining in CHOPS Well Data
UNIVERSITY OF CALGARY Geovisualization for Association Rule Mining in CHOPS Well Data by Xiaodong Sun A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR
More informationAbstract: About the Author:
REMOTE SENSING AND GIS IN LAND USE PLANNING Sathees kumar P 1, Nisha Radhakrishnan 2 1 1 Ph.D Research Scholar, Department of Civil Engineering, National Institute of Technology, Tiruchirappalli- 620015,
More information.. Cal Poly CSC 466: Knowledge Discovery from Data Alexander Dekhtyar..
.. Cal Poly CSC 4: Knowledge Discovery from Data Alexander Dekhtyar.. Data Mining: Mining Association Rules Examples Course Enrollments Itemset. I = { CSC3, CSC3, CSC40, CSC40, CSC4, CSC44, CSC4, CSC44,
More information732A61/TDDD41 Data Mining - Clustering and Association Analysis
732A61/TDDD41 Data Mining - Clustering and Association Analysis Lecture 6: Association Analysis I Jose M. Peña IDA, Linköping University, Sweden 1/14 Outline Content Association Rules Frequent Itemsets
More informationOutline. Fast Algorithms for Mining Association Rules. Applications of Data Mining. Data Mining. Association Rule. Discussion
Outline Fast Algorithms for Mining Association Rules Rakesh Agrawal Ramakrishnan Srikant Introduction Algorithm Apriori Algorithm AprioriTid Comparison of Algorithms Conclusion Presenter: Dan Li Discussion:
More informationExploring Spatial Relationships for Knowledge Discovery in Spatial Data
2009 International Conference on Computer Engineering and Applications IPCSIT vol.2 (2011) (2011) IACSIT Press, Singapore Exploring Spatial Relationships for Knowledge Discovery in Spatial Norazwin Buang
More informationCOMP 5331: Knowledge Discovery and Data Mining
COMP 5331: Knowledge Discovery and Data Mining Acknowledgement: Slides modified by Dr. Lei Chen based on the slides provided by Tan, Steinbach, Kumar And Jiawei Han, Micheline Kamber, and Jian Pei 1 10
More informationDetecting Anomalous and Exceptional Behaviour on Credit Data by means of Association Rules. M. Delgado, M.D. Ruiz, M.J. Martin-Bautista, D.
Detecting Anomalous and Exceptional Behaviour on Credit Data by means of Association Rules M. Delgado, M.D. Ruiz, M.J. Martin-Bautista, D. Sánchez 18th September 2013 Detecting Anom and Exc Behaviour on
More informationEFFICIENT MINING OF WEIGHTED QUANTITATIVE ASSOCIATION RULES AND CHARACTERIZATION OF FREQUENT ITEMSETS
EFFICIENT MINING OF WEIGHTED QUANTITATIVE ASSOCIATION RULES AND CHARACTERIZATION OF FREQUENT ITEMSETS Arumugam G Senior Professor and Head, Department of Computer Science Madurai Kamaraj University Madurai,
More informationDATA MINING LECTURE 3. Frequent Itemsets Association Rules
DATA MINING LECTURE 3 Frequent Itemsets Association Rules This is how it all started Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases.
More informationData Mining. Dr. Raed Ibraheem Hamed. University of Human Development, College of Science and Technology Department of Computer Science
Data Mining Dr. Raed Ibraheem Hamed University of Human Development, College of Science and Technology Department of Computer Science 2016 2017 Road map The Apriori algorithm Step 1: Mining all frequent
More informationQuantitative Association Rule Mining on Weighted Transactional Data
Quantitative Association Rule Mining on Weighted Transactional Data D. Sujatha and Naveen C. H. Abstract In this paper we have proposed an approach for mining quantitative association rules. The aim of
More informationMining Infrequent Patter ns
Mining Infrequent Patter ns JOHAN BJARNLE (JOHBJ551) PETER ZHU (PETZH912) LINKÖPING UNIVERSITY, 2009 TNM033 DATA MINING Contents 1 Introduction... 2 2 Techniques... 3 2.1 Negative Patterns... 3 2.2 Negative
More informationApriori algorithm. Seminar of Popular Algorithms in Data Mining and Machine Learning, TKK. Presentation Lauri Lahti
Apriori algorithm Seminar of Popular Algorithms in Data Mining and Machine Learning, TKK Presentation 12.3.2008 Lauri Lahti Association rules Techniques for data mining and knowledge discovery in databases
More informationAssociation Analysis. Part 1
Association Analysis Part 1 1 Market-basket analysis DATA: A large set of items: e.g., products sold in a supermarket A large set of baskets: e.g., each basket represents what a customer bought in one
More informationModified Entropy Measure for Detection of Association Rules Under Simpson's Paradox Context.
Modified Entropy Measure for Detection of Association Rules Under Simpson's Paradox Context. Murphy Choy Cally Claire Ong Michelle Cheong Abstract The rapid explosion in retail data calls for more effective
More informationAssociation Rule. Lecturer: Dr. Bo Yuan. LOGO
Association Rule Lecturer: Dr. Bo Yuan LOGO E-mail: yuanb@sz.tsinghua.edu.cn Overview Frequent Itemsets Association Rules Sequential Patterns 2 A Real Example 3 Market-Based Problems Finding associations
More informationSpatial Co-location Patterns Mining
Spatial Co-location Patterns Mining Ruhi Nehri Dept. of Computer Science and Engineering. Government College of Engineering, Aurangabad, Maharashtra, India. Meghana Nagori Dept. of Computer Science and
More informationSPATIAL DATA MINING. Ms. S. Malathi, Lecturer in Computer Applications, KGiSL - IIM
SPATIAL DATA MINING Ms. S. Malathi, Lecturer in Computer Applications, KGiSL - IIM INTRODUCTION The main difference between data mining in relational DBS and in spatial DBS is that attributes of the neighbors
More information43400 Serdang Selangor, Malaysia Serdang Selangor, Malaysia 4
An Extended ID3 Decision Tree Algorithm for Spatial Data Imas Sukaesih Sitanggang # 1, Razali Yaakob #2, Norwati Mustapha #3, Ahmad Ainuddin B Nuruddin *4 # Faculty of Computer Science and Information
More informationData Mining Concepts & Techniques
Data Mining Concepts & Techniques Lecture No. 04 Association Analysis Naeem Ahmed Email: naeemmahoto@gmail.com Department of Software Engineering Mehran Univeristy of Engineering and Technology Jamshoro
More informationD B M G Data Base and Data Mining Group of Politecnico di Torino
Data Base and Data Mining Group of Politecnico di Torino Politecnico di Torino Association rules Objective extraction of frequent correlations or pattern from a transactional database Tickets at a supermarket
More informationDistributed Mining of Frequent Closed Itemsets: Some Preliminary Results
Distributed Mining of Frequent Closed Itemsets: Some Preliminary Results Claudio Lucchese Ca Foscari University of Venice clucches@dsi.unive.it Raffaele Perego ISTI-CNR of Pisa perego@isti.cnr.it Salvatore
More informationCHAPTER 2: DATA MINING - A MODERN TOOL FOR ANALYSIS. Due to elements of uncertainty many problems in this world appear to be
11 CHAPTER 2: DATA MINING - A MODERN TOOL FOR ANALYSIS Due to elements of uncertainty many problems in this world appear to be complex. The uncertainty may be either in parameters defining the problem
More informationAssociation Rules. Fundamentals
Politecnico di Torino Politecnico di Torino 1 Association rules Objective extraction of frequent correlations or pattern from a transactional database Tickets at a supermarket counter Association rule
More informationMining Positive and Negative Fuzzy Association Rules
Mining Positive and Negative Fuzzy Association Rules Peng Yan 1, Guoqing Chen 1, Chris Cornelis 2, Martine De Cock 2, and Etienne Kerre 2 1 School of Economics and Management, Tsinghua University, Beijing
More informationLecture Notes for Chapter 6. Introduction to Data Mining
Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004
More informationWastelands Analysis and Mapping of Bhiwani District, Haryana
Wastelands Analysis and Mapping of Bhiwani District, Haryana Virender Sihag Research Scholar, Department of Geography, OPJS University, Churu, Rajasthan ABSTRACT This study aimed at monitoring, mapping,
More informationD B M G. Association Rules. Fundamentals. Fundamentals. Elena Baralis, Silvia Chiusano. Politecnico di Torino 1. Definitions.
Definitions Data Base and Data Mining Group of Politecnico di Torino Politecnico di Torino Itemset is a set including one or more items Example: {Beer, Diapers} k-itemset is an itemset that contains k
More informationD B M G. Association Rules. Fundamentals. Fundamentals. Association rules. Association rule mining. Definitions. Rule quality metrics: example
Association rules Data Base and Data Mining Group of Politecnico di Torino Politecnico di Torino Objective extraction of frequent correlations or pattern from a transactional database Tickets at a supermarket
More informationDECISION MAKING IN AGRICULTURE BASED ON LAND SUITABILITY SPATIAL DATA ANALYSIS APPROACH
DECISION MAKING IN AGRICULTURE BASED ON LAND SUITABILITY SPATIAL DATA ANALYSIS APPROACH 1 M. PARIMALA, 2 DAPHNE LOPEZ 1 Asst Prof., School of Information Technology & Engineeirng, VIT University 2 Prof.,
More informationCOMP 5331: Knowledge Discovery and Data Mining
COMP 5331: Knowledge Discovery and Data Mining Acknowledgement: Slides modified by Dr. Lei Chen based on the slides provided by Jiawei Han, Micheline Kamber, and Jian Pei And slides provide by Raymond
More informationMachine Learning: Pattern Mining
Machine Learning: Pattern Mining Information Systems and Machine Learning Lab (ISMLL) University of Hildesheim Wintersemester 2007 / 2008 Pattern Mining Overview Itemsets Task Naive Algorithm Apriori Algorithm
More informationCS 484 Data Mining. Association Rule Mining 2
CS 484 Data Mining Association Rule Mining 2 Review: Reducing Number of Candidates Apriori principle: If an itemset is frequent, then all of its subsets must also be frequent Apriori principle holds due
More informationAlgorithms for Characterization and Trend Detection in Spatial Databases
Published in Proceedings of 4th International Conference on Knowledge Discovery and Data Mining (KDD-98) Algorithms for Characterization and Trend Detection in Spatial Databases Martin Ester, Alexander
More informationTo Predict Rain Fall in Desert Area of Rajasthan Using Data Mining Techniques
To Predict Rain Fall in Desert Area of Rajasthan Using Data Mining Techniques Peeyush Vyas Asst. Professor, CE/IT Department of Vadodara Institute of Engineering, Vadodara Abstract: Weather forecasting
More informationEnhancement of Effective Spatial Data Analysis using R
Indian Journal of Science and Technology, Vol 9(21), DOI: 10.17485/ijst/2016/v9i21/95149, June 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Enhancement of Effective Spatial Data Analysis using
More information10/19/2017 MIST.6060 Business Intelligence and Data Mining 1. Association Rules
10/19/2017 MIST6060 Business Intelligence and Data Mining 1 Examples of Association Rules Association Rules Sixty percent of customers who buy sheets and pillowcases order a comforter next, followed by
More informationEncyclopedia of Machine Learning Chapter Number Book CopyRight - Year 2010 Frequent Pattern. Given Name Hannu Family Name Toivonen
Book Title Encyclopedia of Machine Learning Chapter Number 00403 Book CopyRight - Year 2010 Title Frequent Pattern Author Particle Given Name Hannu Family Name Toivonen Suffix Email hannu.toivonen@cs.helsinki.fi
More informationDATA MINING - 1DL360
DATA MINING - 1DL36 Fall 212" An introductory class in data mining http://www.it.uu.se/edu/course/homepage/infoutv/ht12 Kjell Orsborn Uppsala Database Laboratory Department of Information Technology, Uppsala
More informationA Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases
A Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases L.K. Sharma 1, O. P. Vyas 1, U. S. Tiwary 2, R. Vyas 1 1 School of Studies in Computer Science Pt. Ravishankar
More informationData Warehousing & Data Mining
Data Warehousing & Data Mining Wolf-Tilo Balke Kinda El Maarry Institut für Informationssysteme Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de 9. Business Intelligence 9. Business Intelligence
More informationSPACING OF RURAL SETTLEMENTS IN NAGAUR DISTRICT, RAJASTHAN: A SPATIAL ANALYSIS
Journal of Global Resources Volume 5 July 2017 Page 28-33 ISSN: 2395-3160 (Print), 2455-2445 (Online) 05 SPACING OF RURAL SETTLEMENTS IN NAGAUR DISTRICT, RAJASTHAN: A SPATIAL ANALYSIS Varun Binda Assistant
More informationData Warehousing & Data Mining
9. Business Intelligence Data Warehousing & Data Mining Wolf-Tilo Balke Silviu Homoceanu Institut für Informationssysteme Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de 9. Business Intelligence
More informationCS 584 Data Mining. Association Rule Mining 2
CS 584 Data Mining Association Rule Mining 2 Recall from last time: Frequent Itemset Generation Strategies Reduce the number of candidates (M) Complete search: M=2 d Use pruning techniques to reduce M
More informationCLICK HERE TO KNOW MORE
CLICK HERE TO KNOW MORE Geoinformatics Applications in Land Resources Management G.P. Obi Reddy National Bureau of Soil Survey & Land Use Planning Indian Council of Agricultural Research Amravati Road,
More informationAnalysis of Land Use And Land Cover Changes Using Gis, Rs And Determination of Deforestation Factors Using Unsupervised Classification And Clustering
IOSR Journal of Applied Geology and Geophysics (IOSR-JAGG) e-issn: 2321 0990, p-issn: 2321 0982.Volume 5, Issue 3 Ver. II (May - June 2017), PP 73-78 www.iosrjournals.org Analysis of Land Use And Land
More informationAssociation Rule Mining on Web
Association Rule Mining on Web What Is Association Rule Mining? Association rule mining: Finding interesting relationships among items (or objects, events) in a given data set. Example: Basket data analysis
More informationLars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany
Syllabus Fri. 21.10. (1) 0. Introduction A. Supervised Learning: Linear Models & Fundamentals Fri. 27.10. (2) A.1 Linear Regression Fri. 3.11. (3) A.2 Linear Classification Fri. 10.11. (4) A.3 Regularization
More informationGeospatial data pre-processing on watershed datasets: A GIS approach
Edith Cowan University Research Online ECU Publications Post 2013 2014 Geospatial data pre-processing on watershed datasets: A GIS approach Sreedhar Nallan Edith Cowan University, acharyasree@gmail.com
More informationINTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 2, No 1, 2011
INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 2, No 1, 2011 Copyright 2010 All rights reserved Integrated Publishing services Research article ISSN 0976 4380 Spatio-Temporal changes of Land
More informationMining Class-Dependent Rules Using the Concept of Generalization/Specialization Hierarchies
Mining Class-Dependent Rules Using the Concept of Generalization/Specialization Hierarchies Juliano Brito da Justa Neves 1 Marina Teresa Pires Vieira {juliano,marina}@dc.ufscar.br Computer Science Department
More informationHandling a Concept Hierarchy
Food Electronics Handling a Concept Hierarchy Bread Milk Computers Home Wheat White Skim 2% Desktop Laptop Accessory TV DVD Foremost Kemps Printer Scanner Data Mining: Association Rules 5 Why should we
More informationAssociation Rules Information Retrieval and Data Mining. Prof. Matteo Matteucci
Association Rules Information Retrieval and Data Mining Prof. Matteo Matteucci Learning Unsupervised Rules!?! 2 Market-Basket Transactions 3 Bread Peanuts Milk Fruit Jam Bread Jam Soda Chips Milk Fruit
More informationIntroduction to Spatial Data Mining
Introduction to Spatial Data Mining 7.1 Pattern Discovery 7.2 Motivation 7.3 Classification Techniques 7.4 Association Rule Discovery Techniques 7.5 Clustering 7.6 Outlier Detection Introduction: a classic
More information1 Frequent Pattern Mining
Decision Support Systems MEIC - Alameda 2010/2011 Homework #5 Due date: 31.Oct.2011 1 Frequent Pattern Mining 1. The Apriori algorithm uses prior knowledge about subset support properties. In particular,
More informationAn Optimized Interestingness Hotspot Discovery Framework for Large Gridded Spatio-temporal Datasets
IEEE Big Data 2015 Big Data in Geosciences Workshop An Optimized Interestingness Hotspot Discovery Framework for Large Gridded Spatio-temporal Datasets Fatih Akdag and Christoph F. Eick Department of Computer
More informationDATA MINING - 1DL360
DATA MINING - DL360 Fall 200 An introductory class in data mining http://www.it.uu.se/edu/course/homepage/infoutv/ht0 Kjell Orsborn Uppsala Database Laboratory Department of Information Technology, Uppsala
More informationA Survey: On Spatial Data Mining
A Survey: On Spatial Data Mining Asmita Bist 1, Mainaz Faridi 2 M.Tech Scholar 1, Assistant Professor 2 Department Of Computer Science, AIM & ACT, Banasthali University, Rajasthan, India Abstract The extraction
More informationINTRODUCTION TO GEOGRAPHIC INFORMATION SYSTEM By Reshma H. Patil
INTRODUCTION TO GEOGRAPHIC INFORMATION SYSTEM By Reshma H. Patil ABSTRACT:- The geographical information system (GIS) is Computer system for capturing, storing, querying analyzing, and displaying geospatial
More informationUNION TERRITORIES Daman Diu Lakshadweep Karaikal Mahe Pondicherry Yanam Total
Category Andaman & Nicobar Chandigarh Dadra & Nagar Haveli Table 66: Distribution of Wastelands UNION TERRITORIES Daman Diu Lakshadweep Karaikal Mahe Pondicherry Yanam Total 1 0.00 0.08 0.01 0.16 0.00
More informationDATA MINING LECTURE 4. Frequent Itemsets, Association Rules Evaluation Alternative Algorithms
DATA MINING LECTURE 4 Frequent Itemsets, Association Rules Evaluation Alternative Algorithms RECAP Mining Frequent Itemsets Itemset A collection of one or more items Example: {Milk, Bread, Diaper} k-itemset
More informationData Mining and Analysis: Fundamental Concepts and Algorithms
Data Mining and Analysis: Fundamental Concepts and Algorithms dataminingbook.info Mohammed J. Zaki 1 Wagner Meira Jr. 2 1 Department of Computer Science Rensselaer Polytechnic Institute, Troy, NY, USA
More informationLAND SUITABILITY STUDY IN LAND DEGRADED AREA DUE TO MINING IN DHANBAD DISTRICT, JHARKHAND.
LAND SUITABILITY STUDY IN LAND DEGRADED AREA DUE TO MINING IN DHANBAD DISTRICT, JHARKHAND. Saranathan, E a*, Loveson, V.J b. and Victor Rajamanickam, G c a School of Civil Engineering, SASTRA, Thanjavur
More informationASSOCIATION RULE MINING BASED ANALYSIS ON HOROSCOPE DATA A PERSPECTIVE STUDY
International Journal of Computer Engineering & Technology (IJCET) Volume 8, Issue 3, May-June 2017, pp. 76 81, Article ID: IJCET_08_03_008 Available online at http://www.iaeme.com/ijcet/issues.asp?jtype=ijcet&vtype=8&itype=3
More informationEffect of land use/land cover changes on runoff in a river basin: a case study
Water Resources Management VI 139 Effect of land use/land cover changes on runoff in a river basin: a case study J. Letha, B. Thulasidharan Nair & B. Amruth Chand College of Engineering, Trivandrum, Kerala,
More informationReductionist View: A Priori Algorithm and Vector-Space Text Retrieval. Sargur Srihari University at Buffalo The State University of New York
Reductionist View: A Priori Algorithm and Vector-Space Text Retrieval Sargur Srihari University at Buffalo The State University of New York 1 A Priori Algorithm for Association Rule Learning Association
More informationSummary. 8.1 BI Overview. 8. Business Intelligence. 8.1 BI Overview. 8.1 BI Overview 12/17/ Business Intelligence
Summary Data Warehousing & Data Mining Wolf-Tilo Balke Silviu Homoceanu Institut für Informationssysteme Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de How to build a DW The DW Project:
More informationA Novel Dencos Model For High Dimensional Data Using Genetic Algorithms
A Novel Dencos Model For High Dimensional Data Using Genetic Algorithms T. Vijayakumar 1, V.Nivedhitha 2, K.Deeba 3 and M. Sathya Bama 4 1 Assistant professor / Dept of IT, Dr.N.G.P College of Engineering
More informationAbstract. TECHNOFAME- A Journal of Multidisciplinary Advance Research. Vol.2 No. 2, (2013) Received: Feb.2013; Accepted Oct.
Vol.2 No. 2, 83-87 (2013) Received: Feb.2013; Accepted Oct. 2013 Landuse Pattern Analysis Using Remote Sensing: A Case Study of Morar Block, of Gwalior District, M.P. Subhash Thakur 1 Akhilesh Singh 2
More informationChapters 6 & 7, Frequent Pattern Mining
CSI 4352, Introduction to Data Mining Chapters 6 & 7, Frequent Pattern Mining Young-Rae Cho Associate Professor Department of Computer Science Baylor University CSI 4352, Introduction to Data Mining Chapters
More informationIntroducing GIS analysis
1 Introducing GIS analysis GIS analysis lets you see patterns and relationships in your geographic data. The results of your analysis will give you insight into a place, help you focus your actions, or
More informationMining Spatial Trends by a Colony of Cooperative Ant Agents
Mining Spatial Trends by a Colony of Cooperative Ant Agents Ashan Zarnani Masoud Rahgozar Abstract Large amounts of spatially referenced data has been aggregated in various application domains such as
More informationMining Molecular Fragments: Finding Relevant Substructures of Molecules
Mining Molecular Fragments: Finding Relevant Substructures of Molecules Christian Borgelt, Michael R. Berthold Proc. IEEE International Conference on Data Mining, 2002. ICDM 2002. Lecturers: Carlo Cagli
More informationDATA MINING LECTURE 4. Frequent Itemsets and Association Rules
DATA MINING LECTURE 4 Frequent Itemsets and Association Rules This is how it all started Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases.
More informationData Warehousing. Wolf-Tilo Balke Silviu Homoceanu Institut für Informationssysteme Technische Universität Braunschweig
Data Warehousing & Data Mining Wolf-Tilo Balke Silviu Homoceanu Institut für Informationssysteme Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de Summary How to build a DW The DW Project:
More informationMeelis Kull Autumn Meelis Kull - Autumn MTAT Data Mining - Lecture 05
Meelis Kull meelis.kull@ut.ee Autumn 2017 1 Sample vs population Example task with red and black cards Statistical terminology Permutation test and hypergeometric test Histogram on a sample vs population
More informationBasic Data Structures and Algorithms for Data Profiling Felix Naumann
Basic Data Structures and Algorithms for 8.5.2017 Overview 1. The lattice 2. Apriori lattice traversal 3. Position List Indices 4. Bloom filters Slides with Thorsten Papenbrock 2 Definitions Lattice Partially
More informationMining Rank Data. Sascha Henzgen and Eyke Hüllermeier. Department of Computer Science University of Paderborn, Germany
Mining Rank Data Sascha Henzgen and Eyke Hüllermeier Department of Computer Science University of Paderborn, Germany {sascha.henzgen,eyke}@upb.de Abstract. This paper addresses the problem of mining rank
More informationInternational Journal of Intellectual Advancements and Research in Engineering Computations
ISSN:2348-2079 Volume-5 Issue-2 International Journal of Intellectual Advancements and Research in Engineering Computations Agricultural land investigation and change detection in Coimbatore district by
More informationSPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM
SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM S. Khoshahval a *, M. Farnaghi a, M. Taleai a a Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran
More informationLanduse and Landcover change analysis in Selaiyur village, Tambaram taluk, Chennai
Landuse and Landcover change analysis in Selaiyur village, Tambaram taluk, Chennai K. Ilayaraja Department of Civil Engineering BIST, Bharath University Selaiyur, Chennai 73 ABSTRACT The synoptic picture
More informationData mining, 4 cu Lecture 5:
582364 Data mining, 4 cu Lecture 5: Evaluation of Association Patterns Spring 2010 Lecturer: Juho Rousu Teaching assistant: Taru Itäpelto Evaluation of Association Patterns Association rule algorithms
More informationART Based Reliable Method for Prediction of Agricultural Land Changes Using Remote Sensing
Circuits and Systems, 2016, 7, 1051-1067 Published Online May 2016 in SciRes. http://www.scirp.org/journal/cs http://dx.doi.org/10.4236/cs.2016.76089 ART Based Reliable Method for Prediction of Agricultural
More informationClustering Assisted Co-location Pattern Mining for Spatial Data
Clustering Assisted Co-location Pattern Mining for Spatial Data Naveen Kumar Department of Computer Science, Pondicherry University, Kalapet - 605014, Puducherry, India. E-mail: nav.bharti@gmail.com S.
More informationMining Climate Data. Michael Steinbach Vipin Kumar University of Minnesota /AHPCRC
Mining Climate Data Michael Steinbach Vipin Kumar University of Minnesota /AHPCRC Collaborators: G. Karypis, S. Shekhar (University of Minnesota/AHPCRC) V. Chadola, S. Iyer, G. Simon, P. Zhang (UM/AHPCRC)
More informationAccuracy Assessment of Land Cover Classification in Jodhpur City Using Remote Sensing and GIS
Accuracy Assessment of Land Cover Classification in Jodhpur City Using Remote Sensing and GIS S.L. Borana 1, S.K.Yadav 1 Scientist, RSG, DL, Jodhpur, Rajasthan, India 1 Abstract: A This study examines
More informationAssignment 7 (Sol.) Introduction to Data Analytics Prof. Nandan Sudarsanam & Prof. B. Ravindran
Assignment 7 (Sol.) Introduction to Data Analytics Prof. Nandan Sudarsanam & Prof. B. Ravindran 1. Let X, Y be two itemsets, and let denote the support of itemset X. Then the confidence of the rule X Y,
More informationChange Detection Across Geographical System of Land using High Resolution Satellite Imagery
IJCTA, 9(40), 2016, pp. 129-139 International Science Press Closed Loop Control of Soft Switched Forward Converter Using Intelligent Controller 129 Change Detection Across Geographical System of using
More informationUrban Hydrology - A Case Study On Water Supply And Sewage Network For Madurai Region, Using Remote Sensing & GIS Techniques
RESEARCH INVENTY: International Journal of Engineering and Science ISBN: 2319-6483, ISSN: 2278-4721, Vol. 1, Issue 8 (November 2012), PP 07-12 www.researchinventy.com Urban Hydrology - A Case Study On
More informationInternational Journal of Advance Engineering and Research Development. Review Paper On Weather Forecast Using cloud Computing Technique
Scientific Journal of Impact Factor (SJIF): 4.72 International Journal of Advance Engineering and Research Development Volume 4, Issue 12, December -2017 e-issn (O): 2348-4470 p-issn (P): 2348-6406 Review
More informationUrban Expansion and Loss of Agricultural Land: A Remote Sensing Based Study of Shirpur City, Maharashtra
Cloud Publications International Journal of Advanced Remote Sensing and GIS 2017, Volume 6, Issue 1, pp. 2097-2102 ISSN 2320 0243, Crossref: 10.23953/cloud.ijarsg.113 Research Article Open Access Urban
More information