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Association Analysis - Unsupervised Learning Association...

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Unsupervised Learning - Association Analysis Section 1 Introduction Page 2 Section 2 Market Basket Analysis Page 2 Section 3 Using Association Node Page 6 Section 4 Understanding Association Rules Page 9 Section 5 Association Analysis for Non-Binary Variables Page 12 Section 6 Disassociation Analysis Page 16 Section 7 Sequential Association Analysis Page 20 Section 8 Case Study Page 24 Appendix 1 A priori Algorithm Page 28 Appendix 2 SAS Code used in Example 3 Page 29 Appendix 3 Data Set “Income.txt” Page 30 Appendix 4 SAS Code for Disassociation Node Page 33 Appendix 5 Sample SAS Code for Case Study Page 34 Appendix 6 References Page 35
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Section 1 Introduction One significant advance in data mining at the end of the 20 th century is that “association rules analysis” has emerged as a popular tool for mining a very large scale commercial database (say, the number of variables is greater than10 4 and the number of observations is greater than 10 8 ). Association mining attempts to construct simple “rules” (descriptive statistics) that describe regions of relatively high density in a very large commercial database. When all variables in the database are binary, the association rules analysis can also be referred to as “market basket analysis”. For example, consider the sales database of an on-line bookstore, where the objects represent customers and the attributes represent authors and/or books. The rules to be discovered are the set of books most frequently bought together by the customers. An example could be that, “15% of the people who buy Dorian Pyle’s Data Preparation for Data Mining also buy Data Mining Techniques by Berry and Linoff.” The retail stores can use the knowledge discovered from the analysis for enhanced shelf placement, cross marketing, catalog design, and consumer segmentation, etc. Although association analysis has been applied to the retail industry directly, it can be applied to other industries as well. For example, it has been used to predict faults in telecommunication networks. In this session, we will discuss the theoretical foundation of market basket analysis in Section 2. We then use a small commercial banking data set to illustrate how to use Association node in Enterprise Miner to obtain association rules in Section 3. Since association analysis typically produces a very large number of rules, understanding these rules poses a very challenging data analysis task. We will address this issue in Section 4. In Section 5, we extend the use of association analysis to a data set with some non-binary variables. We will address Disassociation Analysis and Sequential Association Analysis in Sections 6 and 7, respectively. We conclude this session with a case study on showing the miner to identify rules found in an association exercise.
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