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

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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. - PowerPoint PPT Presentation

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

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

HW 1

minsup=30%

N

I

F

F5

F7

F5

F9

F6

F3 2

F4

F4

F3

F6

F4

F4

I2

F6

N N NN N N

=> 至少出現 3次

NN

N N N N

I2

I2

F4

I2

F4

N

F: frequent itemsetN: non-considered itemsetI: infrequent candidate

Ans: 16/32

Ans: 11/32

Ans: 5/32

13_

14_

15_

34_

35_

45_

=>L5

=>L1

=>L38

8

=>L9

=>L11

=>L3

minsup=30%

至少出現 3次才是 frequent itemset

I

I

C

C5

C7

C5

C9

F6

MC3 2

F4

F4

MC3

C6

F4

MC4

I2

C6

II

I I I I

I2

I2

MC4

I2

MC4

I

An itemset is closed closed if none of its immediate supersets has the same support as the itemsetAn itemset is maximal frequentmaximal frequent if none of its immediate supersets is frequent

10

II I