Read e-book online Association Rule Mining: Models and Algorithms PDF

By Chengqi Zhang

Due to the recognition of information discovery and information mining, in perform in addition to between educational and company R&D pros, organization rule mining is receiving expanding attention.
The authors current the new growth accomplished in mining quantitative organization principles, causal principles, unparalleled ideas, damaging organization ideas, organization principles in multi-databases, and organization principles in small databases. This booklet is written for researchers, execs, and scholars operating within the fields of information mining, info research, desktop studying, wisdom discovery in databases, and an individual who's attracted to organization rule mining.

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1. 22. The contingency of B and D B ¬B col D 6 0 6 ¬D 1 3 4 row 7 3 10 Now E[B] = O(B) = 7, while E[D] = O(D) = 6. 9167 is significant at the 95% significance level. Furthermore, the largest contribution to the χ2 value comes from the bottomright cell, indicating that the dominant dependence is a veteran, being over 40. This matches our intuition. 2 The FP-tree Based Model Traditional frequent patterns mining adopt an Apriori-like candidate set generation-and-test approach. However, a candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns.

1, itemset {A, B} occurs in the one transaction, T ID = 300. Its frequency is 1, and its support, supp(A∪B), is 25%, which is less than minsupp = 50%. In the formulas used in this book, A ∪ B stands for {A, B}. Itemset {A, C} occurs in the two transactions, T ID = 100 and T ID = 300, its frequency is 2, and its support, supp(A ∪ C), is 50%, which is equal to minsupp = 50%. Itemset {A, D} occurs in the one transaction, T ID = 100. Its frequency is 1, and its support, supp(A ∪ D), is 25%, which is less than minsupp = 50%.

38 2. 15. 16. 17. 18, for the algorithm F requentItemsetsbyP runing. 06 < mininterest, and |p(C ∪ F ) − p(C)p(F )| = 0 < mininterest. Therefore, AB, AC, AD, BF , CD, and CF are not of interest. Hence, AB, AC, AD, BF , CD and CF are pruned from L2 before it is appended into F requentset. 18. 15, the set C3 of the 3-itemsets is: ABC, ABD, ACD, BCD, BCF , BDF , and CDF . 19, for the algorithm F requentItemsets. 19. 07, C3 = {} and L3 = {}, as all 3-itemsets are pruned from C3 because of interest. Thus, Step 2 ends.

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