1. Copyright © 2004 Pearson Education, Inc.

2. Chapter 27 Data Mining Concepts

3. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-3 Overview of Data Mining Technology Data Mining aka Knowledge Discovery in Databases (KDD) –Discovery of new information in terms of patterns or rules from vast amounts of data Must be carried out efficiently on large files and databases

4. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-4 Goals of Data Mining Prediction –Show how certain attributes will behave in future Identification –Identify existance of an item Classification –Partition data into different categories Optimization –Limited resources such as time, space, money

5. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-5 FIGURE 27.1 Example transactions in market-basket model.

6. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-6 FIGURE 27.2 FP-tree and item header table.

7. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-7 FIGURE 27.3 Taxonomy of items in a supermarket.

8. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-8 FIGURE 27.4 Simple hierarchy of soft drinks and chips.

9. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-9 Association Rules Market-Basket Model, Support, and Confidence Apriori Algorithm Sampling Algorithm Frequent-Pattern Tree Algorithm Partition Algorithm Other Types of Association Rules Additional Considerations for Association Rules

10. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-10 Classification The process of learning a model that describes different classes of data. The classes are known in advance – the rules that describe them are not. Mining can help determine past influential characteristics that can be used to predict future behavior.

11. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-11 FIGURE 27.5 Example decision tree for credit card applications.

12. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-12 FIGURE 27.6 Sample training data for classification algorithm.

13. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-13 FIGURE 27.7 Decision tree based on sample training data where the leaf nodes are represented by a set of RIDs of the partitioned records.

14. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-14 Clustering Another way of learning Puts “similar” records into groups –Reaction to medication Similarity function is key

15. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-15 FIGURE 27.8 Sample 2-dimensional records for clustering example (the RID column is not considered).

16. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-16 Approaches to Other Data Mining Problems Discovery of Sequential Patterns Discovery of Patterns in Time Series Regression Neural Networks Genetic Algorithm

17. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-17 Applications of Data Mining Marketing Finance Manufacturing Health Care Probably many other decision-making contexts

18. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-18 Commercial Data Mining Tools Text lists several packages and their strengths Huge field as databases multiply Big potential if you can come up with a way of protecting privacy as well as correcting data.

19. Elmasri and Navathe, Fundamentals of Database Systems, Fourth Edition Copyright © 2004 Pearson Education, Inc. Slide 27-19 Summary Lots of potential in this field Seems complex, but only because of the sheer amount of data. See Wikipedia at –http://en.wikipedia.org/wiki/Data_mininghttp://en.wikipedia.org/wiki/Data_mining