ASSOCIATION 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 Journal Impact Factor (2016): 9.3590(Calculated by GISI) www.jifactor.com ISSN Print: 0976-6367 and ISSN Online: 0976 6375 IAEME Publication ASSOCIATION RULE MINING BASED ANALYSIS ON HOROSCOPE DATA A PERSPECTIVE STUDY Rahul Shajan Research Scholar, Mahatma Gandhi University, Kottayam, Kerala, India Gladston Raj S Head, Department of CS, Govt. College, Nedumangadu, Thiruvananthapuram, Kerala, India ABSTRACT An efficient way of extracting information from huge data repositories is an art. The extraction of similar patterns from a number of transactions is also an important feature of data mining. We have a lot data mining techniques to extract information. Here, we adopt Apriory algorithm, which is one of the popular algorithms mainly used for Association rule mining. The purpose of association rule mining is to find all sets of items that have minimum support specified by the User. These items are the elements of a set called frequent item set. The association rules are generated by using this frequent item set. In astrology, the main database used for prediction is the horoscope of person. The horoscope is a database contains data like the planetary position at the time of birth of an individual. In all the incidents related to a human being, the horoscope of that person will consider to find the chance of happening good and bad things related to that incident. Similarly in some situations we need to find a horoscope that similar to our horoscope. The astrology believers select a life partner who has the similar horoscope (Horoscope matching).in that type of situation, we can utilize the pattern recognition techniques to find the similar horoscopes. In this work, we are trying to study the existing association rule mining algorithm Apriory is sufficient or not to find the similar patterns in the horoscope of different Individuals. Key word: Association rule mining, Apriory Algorithm, Pattern recognition, Horoscope. Cite this Article: Rahul Shajan and Gladston Raj S, Association Rule Mining Based Analysis on Horoscope Data A Perspective Study. International Journal of Computer Engineering & Technology, 8(2), 2017, pp.76 81. http://www.iaeme.com/ijcet/issues.asp?jtype=ijcet&vtype=8&itype=3 http://www.iaeme.com/ijcet/index.asp 76 editor@iaeme.com

Association Rule Mining Based Analysis on Horoscope Data A Perspective Study 1. INTRODUCTION 1.1. Basic Concepts Data mining is the exploration and analysis of large data sets, in order to discover meaningful patterns and rules. The key idea is to find effective ways to combine the computer s power to process data with the human eye s ability to detect patterns [1].Simply data mining is the process of retrieval of user requested data from the huge and complex data repository. Before the extraction of information the data source will undergo the different stages like the data cleaning, integration, pre-processing etc. These tasks will help to remove the errors and ensure consistency. There are two types of mining. First one is descriptive mining, it characterises the general properties of the data in the database. Second one is predictive mining where the current data is used in order to make the predictions. The data mining techniques are widely used to find the similar patterns in large data sets. The scope of pattern recognition is initially identified in business enterprises. Business enterprises are beginning to realize that information on customers and buying patterns are the most valuable information. Association rule mining is the one of the popular tools for pattern recognition. 1.2. Association Rule Mining The problem of finding the associations from data is formulated in 1993 by Agrawal et al. and is often referred to as the Market basket problem [2]. In this problem there is a set of items and large number of transactions. Each of the transaction is the subsets of these items. The purpose is to find the relations between the different items within this transaction or otherwise called it as baskets. The association rule mining is the process of finding the interesting relations among the items. It is a two step process. Finding the frequent item set is the first step and the generation of association rule is the second step in association rule mining. Association rules may be of different types like Boolean and Quantitative. The Boolean rules show the association between the presence and absence of items. The quantitative association rule shows the association between the quantitative items [3]. For a given transaction database T, an association rule is an expression of the form X=>Y, where X and Y are the subsets of A (set of items) and X=>Y holds with confidence c, if c% of transactions in D that support X also support Y. The rule X=>Y has a support s in the transaction set T if s% of transactions in T support X U Y [1]. 1.3. Apriory Algorithm Apriory is a popular algorithm also known as level-wise algorithm [1] to find the frequent item set from a set of transactions. By using this frequent item set the algorithm will generate the association rules. The frequent items should possess a minimum support that should be same or exceeds the support specified by the user. Also the association rules should satisfy the minimum confidence. This algorithm follows a downward closure property [4]. The low support rules generated by the algorithm are uninteresting and it should be avoided. So the support value is a key factor to avoid the uninterested rules. The value minimum confidence which keeps the reliability of the rules generated. In association rule mining both the minimum support and the minimum confidence are important. In this work, we are trying to generate the association rules by finding the similar patterns from the horoscope data of different individuals. 1.4. Horoscope Horoscope is known as the birth chart of an individual. A birth chart shows the position of ten planets (astrology consider the term planet in the sense of influencing factors in macrocosms) in the universe at the time of birth. The ten planets which are considering in the horoscope are http://www.iaeme.com/ijcet/index.asp 77 editor@iaeme.com

Rahul Shajan and Gladston Raj S Sun, Moon, Mars, mercury, Jupiter, Venus, Saturn, Rahu, Kethu (Rahu and Kethu are the two specific points in the universe) and the Gulikan. Besides these planets, the astrologers will find out a specific attribute known as Lagnam. It is calculated by considering the birth time with the sunrise time of the day on which the individual born. A birth chart is a 360 degree chart and it is divided in to twelve houses with 30 degrees each. Aries, Taurus, Gemini, cancer, Leo, Virgo, Libra, Scorpio, Sagittarius, Capricorn, Aquarius, Pisces are the twelve houses. Each of the houses may carry one or more planets and some houses may be empty. In this work, the planets in the different houses are considered as the items and the individuals are considered as the transactions in the sense of association rule mining common practice. 2. LITERATURE SURVEY Abhijith Roarane et al studied the advantages and disadvantages of data mining techniques. The focus here is to know the consumer behaviour, their psychological condition at the time of purchase and how suitable data mining method apply to improve conventional methods [5]. Darshan M. Tank made a detailed study on association rule mining and Apriory algorithm. Here two bottlenecks of frequent item sets mining are the large multitude of candidate item set and poor efficiency of counting their support. He proposes an algorithm which decreases pruning operations of candidate item sets, thereby saving time and increasing efficiency [4]. Neelam Chaplot et al studied the influence of position of planets and stars at the time of birth of a person for predicting the possibility of person become a doctor [10]. 3. METHODOLOGY For the implementation of the Apriory algorithm and the generation of association rules, there should be an item set and a number of transactions. For example, in a sales review of a shop we consider the items sold in that shop as the elements of item set. The different sales transactions are the transaction set. Each transaction set is a subset of the item set and different items may be repeated in different transactions also. We know that in a horoscope we consider the eleven planets including Lagnam. So here, we consider the planets and their position in the different houses. Any of the planets in the horoscope can occupy in any one of the twelve houses. It is clearly depend on the astrological rules. So each planet has twelve possible positional values. We can provide a numerical value for each house. Table 1 Different houses in horoscope and its corresponding numerical values. House Corresponding numerical value Aries 1 Taurus 2 Gemini 3 Cancer 4 Leo 5 Virgo 6 Libra 7 Scorpio 8 Sagittarius 9 Capricorn 10 Aquarius 11 Pisces 12 http://www.iaeme.com/ijcet/index.asp 78 editor@iaeme.com

Association Rule Mining Based Analysis on Horoscope Data A Perspective Study If X is the name of the planet, then it has twelve possible values depend on the house where it occupies. That is X1, X2, X3..., and X12. X3 value means that the planet X is occupied in the third house named Gemini. Similarly other planets also have these twelve possibilities. So a total of 12*12=144 possible values are there in a horoscope. We considered 144 positional values as the items. In the horoscope data analysis by apriory algorithm, the length of the item set is fixed and its size is always 144. Based on this concept, we have prepared a database by collecting the Name, Birth time, Birth date, Birth place and average mark of the student in tenth and plus two classes. By using the Birth details we are able to generate the birth chart and the horoscope details stored in a data base as follows. Figure 1 Database structure. From the figure, it is clear that the attributes kept in our data base are the person id (pid), planets positional value by combining the keyword of planet name with numerical correspondence of Houses (Eg: Mo5 means Moon in House 5 that is Moon in Leo) and the Average mark. The average mark is included in the data base to analyse the educational performance of the students. In astrology, the educational performance of an individual is calculated mainly by the planets Jupiter and Mercury. Also the Bhavam has a great role in the astrological prediction. In a horoscope the Lagnam is always considered as the Bhavam 1, so the house very next to Lgnam is treated as Bhavam 2. Similarly we can calculate the twelve bhavas. Bhavams like 2, 5 and 10 are also considered for the analysis in the context of education. Here we could find out the similar patterns and association rules from the given data base and compare the rules mined with traditional astrological rules. By this analysis we can find out that, the students with same range of mark have any similar patterns in their horoscope data. 4. EXPERIMENTAL ANALYSIS We use the popular data mining application WEKA (Waikato Environment for knowledge Analysis) for the analysis. Initially the collected data undergoes the basic pre-processing activities like data cleaning, integration, transformation etc. The data base file is kept as a http://www.iaeme.com/ijcet/index.asp 79 editor@iaeme.com

Rahul Shajan and Gladston Raj S CSV file and which is loaded to the WEKA application software. Apriory algorithm is selected and executed, which analyses the frequent items and generate the association rules with minimum confidence. 5. RESULTS We got an output window that shows the minimum support and confidence values, total number of cycles performed, size of the set of large item sets and the best rues found. Here the minimum support is 0.6 and the minimum confidence is 0.9. The best rules generated after the execution is shown below. 6. CONCLUSION AND FUTURE WORK In the execution of the algorithm the first step is to check the count value of the occurrence of the item to find the frequent item sets. L k-1 is the frequent item set found in the (k-1) th pass to generate the candidate item set C k, then the support of candidate in C k is counted. In our problem L1, L2 and L3 are generated. Also we got 10 Association rules which have the minimum confidence. There are different rules which show the connection among the Rahu Kethu and Saturn-Rahu-Kethu. But only we have one rule which specifies the relation among the Jupiter and some other planets. Earlier we found that in the context of education and knowledge the key factors are the Jupiter and the Mercury. But there is no rule with Mercury. Also the Bhavam oriented relations are also neglected. That is because Apriory algorithm directly checks the count of items in different transactions and calculates the support and confidence. The presence of same item in different transactions is considered in counting. But in astrology counting is performed by checking the presence of same positional values of planets in different individual s horoscope. If we can apply some conditions in to the algorithm just before the generation of frequent item set and candidate item set, we get more association rules that help to analyse the horoscope of an individual. For that we need to upgrade the Apriory algorithm as a Conditional Apriory Algorithm (CAA). We conclude that the existing Apriory algorithm is not fully capable to analyse the horoscope data in the context of astrology and a new http://www.iaeme.com/ijcet/index.asp 80 editor@iaeme.com

Association Rule Mining Based Analysis on Horoscope Data A Perspective Study upgraded Algorithm can perform effectively in Horoscope data analysis. This type of horoscope analysis and pattern recognition will help to identify an individual with specific type of birth chart. There are so many practical examples for the application of this work like the identification of a life partner with matching horoscope from the huge data repository of Marriage bureau. REFERENCES [1] Arun K Pujari, Data mining techniques, Hyderabad, India, Universities Press Private Ltd, ISBN 978-81-7371-380-4. [2] Aggrawal Charu, and Yu Philip, Mining large item sets for association rules, Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, no.1, March 1998. [3] Smitha T.V. Sundaram, Comparative Study of Data Mining Algorithms For High Dimensional Data Analysis, International Journal of Advances in Engineering & Technology, Vol. 4, Issue 2, Sept 2012.ISSN: 2231-1963. [4] Darshan M. Tank., Improved Apriori Algorithm for Mining Association Rules, International journal of Information Technology and Computer Science, 2014, 07, 15-23,June 2014. [5] Abhijit Raorane & R.V.Kulkarni., Data Mining Techniques: A Source for Consumer Behaviour Analysis, International Journal of Database Management Systems (IJDMS), Vol.3, No.3, August 2011. [6] Pragya Agarwal, Madan Lal Yadav, Nupur Anand. Study on Apriori Algorithm and its Application in Grocery Store. International Journal of Computer Applications (0975 8887) Volume 74 No.14, July 2013. [7] Lin, H., GouminZ., Liu, Q., Application of Apriori Algorithm to Data Mining of the Wildfire, In the proceeding of 6th International Conference on Fuzzy Systems and Knowledge Discovery, 2009, pp.426-429. [8] Maragatham G and Lakshmi M, A Recent Review On Association Rule Mining, Indian Journal of Computer Science and ISSN : 0976-5166E) Vol. 2 No. 6 Dec 2011-Jan 2012. [9] Merseron, A. and Yacef, K., Interestingness Measures for Association Rules in Educational Data, In the proceeding of 1st International Conference on Educational Data Mining, 2008, pp. 1-10. [10] Neelam Chaplot, Praveen Dhyani and O. P. Rishi, Astrological Prediction for Profession Doctor using Classification Techniques of Artificial Intelligence, International Journal of Computer Applications (0975 8887) Volume 122 No.15, July 2015. [11] Aruna J. Chamatkar, Dr. P.K. Butey. A Study on Association Rule Mining With Neural Based Framework. International Journal of Computer Engineering and Technology (IJCET), Volume 5, Issue 9, September (2014), pp. 172-181 [12] Nilamadhab Mishra, Art of Software Defect Association & Correction Using Association Rule Mining. International Journal of Computer Engineering and Technology (IJCET), Volume 1, Number 1, May - June (2010 [13] M. Venkatesh, Dr. M. Krishnamurthy, Mining Association Rules For High Utility Item sets Using Up Growth+Algorithm From Transactional Databases. International Journal of Computer Engineering and Technology (IJCET), Volume 5, Issue 3, March (2014), pp. 164-173 http://www.iaeme.com/ijcet/index.asp 81 editor@iaeme.com