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

Introduction to Data Mining Pang-Ning Tan, Michael Steinbach, Vipin Kumar. HW 1. N. N. N. N. N. N. N. N. N. N. N. N. N. 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.

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

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  1. Introduction to Data Mining Pang-Ning Tan, Michael Steinbach, Vipin Kumar HW 1

  2. N N N N N N N N N N N N N 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 F I 2 2 4 2 4 I F I I F N

  3. Ans: 16/32 Ans: 11/32 Ans: 5/32

  4. 13_ =>L5 14_ =>L1 15_ 8 =>L3 34_ =>L9 35_ =>L11 45_ 8 =>L3

  5. I I I I I I I I I I 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 MC C F I 2 2 4 2 4 I MC I I MC I

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