chap05_new - Mining Frequent Patterns Associations and...

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二〇一七年五月三十一日 Data Mining: Concepts and Techniques 1 Mining Frequent Patterns, Associations, and Correlations (Chapter 5)
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二〇一七年五月三十一日 Data Mining: Concepts and Techniques 2 Chapter 5 Mining Frequent Patterns, Associations, and Correlations What is association rule mining Mining single-dimensional Boolean association rules Mining multilevel association rules Mining multidimensional association rules From association mining to correlation analysis Summary
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二〇一七年五月三十一日 Data Mining: Concepts and Techniques 3 What Is Association Mining? Association rule mining: Finding frequent patterns, associations, correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories. Rule form: Body   ead [support, confidence] . Examples. buys(x, “diapers”) buys(x, “beers”) [0.5%, 60%] age(X, “20..29”) ^ income(X, “30..39K”) buys(X, “PC”) [2%, 60%] Applications: Basket data analysis, cross-marketing, catalog design, loss-leader analysis, classification, etc.
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二〇一七年五月三十一日 Introduction to Data Mining 4 Denotation of Association Rules Rule form: Body   ead [support, confidence] . (association, correlation and causality) Rule examples sales(T, “computer”) sales(T, “software”) [support = 1%, confidence = 75%] buy(T, “Beer”) buy(T, “Diaper”) [support = 2%, confidence = 70%] age(X, “20..29”) ^ income(X, “30..39K”) buys(X, “PC”) [support = 2%, confidence = 60%] data context attribute name data content data object
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二〇一七年五月三十一日 Data Mining: Concepts and Techniques 5 Association Rule Mining ( Basic Concepts ) Given: a database or set of transactions each transaction is a list of items (e.g., purchased by a customer in a visit) Find: all rules that correlate the presence of a set of items with that of another set of items ( X Y ) E.g., 98% of people who purchase tires and auto accessories also get car maintenance done Application examples ? Car Maintenance Agreement (What the store should do to boost Car Maintenance Agreement sales) Home Electronics ? (What other products should the store stocks up?)
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Association Rule Mining ( Support and Confidence ) Given a transaction D/B, find all the rules X Y with minimum support and confidence support S : probability that a transaction contains {X & Y } confidence C : conditional probability that a transaction having {X} also contains Y I = {i 1 ,i 2 ,i 3 , ...,i n } : set of all items T j I : a transaction, A C (50%, 66.6%) C A (50%, 100%) Customer buys X Customer buys both Customer buys Y n 1 j j T I
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二〇一七年五月三十一日 Data Mining: Concepts and Techniques 7 A Road Map for Association Rule Mining ( Types of Association Rules ) Boolean vs. quantitative associations (based on the types of data values) buys(x, “SQLServer”) ^ buys(x, “DMBook”) buys(x, “DBMiner”) [0.2%, 60%] age(x, “30..39”) ^ income(x, “42..48K”)
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