Data Mining. Chapter 1. What s it all about?

Size: px

Start display at page:

Download "Data Mining. Chapter 1. What s it all about?"

Martina Greene
6 years ago
Views:

1 Data Mining Chapter 1. What s it all about? 1

2 DM & ML Ubiquitous computing environment Excessive amount of data (data flooding) Gap between the generation of data and their understanding Looking for structural patterns in data i.e., intelligently analyzed data Data mining The process of discovering patterns in data Pattern: making useful predictions on new data Structural! (capturing the decision structure) 2

3 DM & ML Structural patterns Table 1.1 Contact Lens Data Recommendation: soft, hard, none e.g.) If tear production rate = reduced, then recommendation = none. Rule: generalizing the missing rows No null values (vs. real-life data sets) 3

4 DM & ML 4

5 Simple examples Attributes : the values of features Measuring different aspects of the instance The weather problem (Table 1.2) Attributes, Outcome Possible combinations : 3 x 3 x 2 x 2 = 36 A rule learned from the information e.g.) If outlook = overcast then play = yes Decision list A set of rules being interpreted in sequence Numeric values in Table 1.3 5

6 Simple examples Table 1.2 The weather data Outlook Temperature Humidity Windy Play Sunny Hot High False No Sunny Hot High True No Overcast Hot High False Yes Rainy Mild High False Yes Rainy Cool Normal False Yes Rainy Cool Normal True No overcast Cool Normal True Yes Sunny Mild High False No Sunny Cool Normal False Yes Rainy Mild Normal False Yes Sunny Mild Normal True Yes Overcast Mild High True Yes Overcast Hot Normal False Yes Rainy Mild High True No 6

7 Simple examples Table 1.3 The weather data with some numeric attributes Outlook Temperature Humidity Windy Play Sunny False No Sunny True No Overcast False Yes Rainy False Yes Rainy False Yes Rainy True No overcast True Yes Sunny False No Sunny False Yes Rainy False Yes Sunny True Yes Overcast True Yes Overcast False Yes Rainy True No 7

8 Simple examples Classification rules vs. Association rules Association rules: strongly associating different attribute values e.g.) IF humidity = normal and windy = false, THEN play = yes IF outlook = sunny and play = no, THEN humidity = high Predicting any of the attributes 8

9 Simple examples More examples Contact lenses, Weather problem Irises: A classic numeric dataset Attributes : numeric Outcome : category Computer configurations Outcome : CPU performance regression (numeric prediction) Labor negotiations (realistic) Outcome : whether the contract is acceptable or not. (by both labor & management)? : unknown or missing values 9

10 Simple examples 10

11 Simple examples 11

12 Simple examples 12

13 Simple examples Soybean classification 35 attributes, 19 disease categories Domain Knowledge IF leaf condition = normal and, IF leaf malformation = absent and, THEN diagnosis is rhizoctonia-root-rot The computer-generated rules outperformed the expert-derived rules. (97.5% vs 72%) 13

14 Simple examples 14

15 Fielded applications Web mining How to rank web pages Decisions involving judgment Whether to lend you money 1,000 training examples of borderline cases 20 attributes : age, years with current employer, Solution: reject all borderline cases? No! Borderline cases are most active customers Learned rules: correct on 70% of cases but human experts only 50% Improving the success rate of the loan decisions, explaining the reasons behind the decision 15

Fielded applications Screening images Detecting oil slicks Oil slicks appear as dark regions with changing size and shape, and few training examples.

16 Fielded applications Screening images Detecting oil slicks Oil slicks appear as dark regions with changing size and shape, and few training examples. Expensive process requiring highly trained personnel Input : a set of raw pixel images from a radar satellite Output : a set of images with putative oil slicks Attributes: size of region, shape, area, intensity, 16

17 Fielded applications Load forecasting An automated load forecasting assistant to determine future demand for power a utility supplier in the electricity industry Given: manually constructed load model that assumes normal climatic conditions Problem: adjust for weather conditions Attributes: temperature, humidity, wind speed, Collecting 15 years data Far quicker (seconds) than trained human forecasters (hours) 17

18 Fielded applications Diagnosis Principal application areas of expert systems Preventative maintenance of electromechanical devices (e.g. 600 faults) Learned rules were slightly superior to the handcrafted ones. The system was put into use because the domain expert approved of the rules. 18

19 Fielded applications Marketing and sales Customer loyalty: identifying customers that are likely to defect by detecting changes in their behavior (e.g. banks/phone companies) Special offers: identifying profitable customers (e.g. reliable owners of credit cards that need extra money during the holiday season) Market basket analysis: Finding groups of items that tend to occur together in transactions of supermarket checkout data Manufacturing, customer support & service, scientific applications, monitoring, etc. 19

20 The data mining process 20

21 21

Tools of AI. Marcin Sydow. Summary. Machine Learning

Tools of AI. Marcin Sydow. Summary. Machine Learning Machine Learning Outline of this Lecture Motivation for Data Mining and Machine Learning Idea of Machine Learning Decision Table: Cases and Attributes Supervised and Unsupervised Learning Classication