Pertemuan XIV FUNGSI MAYOR Assosiation. What Is Association Mining? Association rule mining: –Finding frequent patterns, associations, correlations, or.

Presentasi berjudul: "Pertemuan XIV FUNGSI MAYOR Assosiation. What Is Association Mining? Association rule mining: –Finding frequent patterns, associations, correlations, or."— Transcript presentasi:

1 Pertemuan XIV FUNGSI MAYOR Assosiation

2 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%]

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

4 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

5 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},

6 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

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

8 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)

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

10 Asosiasi dengan Business Intelligence pada SQL Server

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20 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

21 Support (A  B) = P(A  B) Confidence(A  B) = P(B|A)

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