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FUNGSI MAYOR Assosiation

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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. Applications: –Basket data analysis, cross-marketing, catalog design, loss-leader analysis, clustering, classification, etc. Examples. –Rule form: “Body ead [support, confidence]”. –buys(x, “diapers”) buys(x, “beers”) [0.5%, 60%]

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Tugas asosiasi data mining adalah menemukan atribut yang muncul dalam satu waktu.

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Rule Measures: Support and Confidence Find all the rules X & Y Z with minimum confidence and support –support, s, probability that a transaction contains {X Y Z} –confidence, c, conditional probability that a transaction having {X Y} also contains Z Let minimum support 50%, and minimum confidence 50%, we have A C (50%, 66.6%) C A (50%, 100%) Customer buys diaper Customer buys both Customer buys beer

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Association Rule Mining Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction Market-Basket transactions Example of Association Rules {Diaper} {Beer}, {Milk, Bread} {Eggs,Coke}, {Beer, Bread} {Milk},

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Definition: Frequent Itemset Itemset –A collection of one or more items Example: {Milk, Bread, Diaper} –k-itemset An itemset that contains k items Support count ( ) –Frequency of occurrence of an itemset –E.g. ({Milk, Bread,Diaper}) = 2 Support –Fraction of transactions that contain an itemset –E.g. s({Milk, Bread, Diaper}) = 2/5 Frequent Itemset –An itemset whose support is greater than or equal to a minsup threshold

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Definition: Association Rule Example: Example of Rules: {Milk,Beer} {Diaper} {Diaper,Beer} {Milk} {Beer} {Milk,Diaper} {Diaper} {Milk,Beer} {Milk} {Diaper,Beer}

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Definition: Association Rule Example: Example of Rules: {Milk,Beer} {Diaper} {Diaper,Beer} {Milk} {Beer} {Milk,Diaper} {Diaper} {Milk,Beer} {Milk} {Diaper,Beer} (s=0.4, c=1.0) (s=0.4, c=0.67) (s=0.4, c=0.67) (s=0.4, c=0.5) (s=0.4, c=0.5)

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The Apriori Algorithm — Example Database D Scan D C1C1 L1L1 L2L2 C2C2 C2C2 C3C3 L3L3

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Asosiasi dengan Business Intelligence pada SQL Server

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Algoritma Asosiasi MBA (Market Basket Analysis) Langkah-langkah algoritma MBA: 1.Tetapkan besaran dari konsep itemset sering, nilai minimum besaran support dan besaran confidence yang diinginkan. 2.Menetapkan semua itemset sering, yaitu itemset yang memiliki frekuensi itemset minimal sebesar bilangan sebelumnya. 3.Dari semua itemset sering, hasilkan aturan asosiasi yang memenuhi nilai minimum support dan confidence

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Support (A B) = P(A B) Confidence(A B) = P(B|A)

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Association rule mining Goal: Find all rules that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf). Assume all data.

Association rule mining Goal: Find all rules that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf). Assume all data.

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