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**Introduction to Data Mining Pang-Ning Tan, Michael Steinbach, Vipin Kumar**

HW 1

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**minsup=30% => 至少出現3次 F 5 7 5 9 6 F F F F F 3 2 4 4 3 6 4 4 2 6 F I**

F: frequent itemset N: non-considered itemset I: infrequent candidate minsup=30% => 至少出現3次 F 5 7 5 9 6 F F F F F 3 2 4 4 3 6 4 4 2 6 F I F F F F F F I F N N 2 2 4 2 4 I I F I F N N N

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Ans: 16/32 Ans: 11/32 Ans: 5/32

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13_ =>L5 14_ =>L1 15_ 8 =>L3 34_ =>L9 35_ =>L11 45_ 8 =>L3

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**10 C 5 7 5 9 6 C C C C F minsup=30% 3 2 4 4 3 6 4 4 2 6 至少出現3次才是**

An itemset is maximal frequent if none of its immediate supersets is frequent An itemset is closed if none of its immediate supersets has the same support as the itemset 10 C 5 7 5 9 6 C C C C F minsup=30% 3 2 4 4 3 6 4 4 2 6 至少出現3次才是 frequent itemset MC I F F MC C F MC I C I I 2 2 4 2 4 I I MC I MC I I I

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© Tan,Steinbach, Kumar Introduction to Data Mining 1/17/2006 1 Data Mining Cluster Analysis: Advanced Concepts and Algorithms Figures for Chapter 9 Introduction.

© Tan,Steinbach, Kumar Introduction to Data Mining 1/17/2006 1 Data Mining Cluster Analysis: Advanced Concepts and Algorithms Figures for Chapter 9 Introduction.

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