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Data processing of frequent itemsets

2014-10-15 21:45:48
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aprioriapriori MatlabMatlab


Achieving goals 1 ), Each user downloads movies collection is considered to be a shopping basket 2 )求 k=2 Frequent itemsets, and support is given We adopt Apriori algorithm Apriori algorithm Uses a bottom-up approach, from the 1- Frequent start, gradually find out high frequency set. Apriori algorithm can be divided into two steps: (1) generating frequent itemsets, and (2) generates all the credible from the frequent item set Mining Association Rules (referring to its confidence level above minconf). Apriori algorithm generates all frequent itemsets based on deductive principles: as a frequent item sets meet the minimum support requirements, it meets all nonempty subsets of minimum support requirements. In order to simplify handling,Apriori algorithm assumes that all project items in a set are in the dictionary order. It uses a sequential search: frequent itemsets based on frequent itemsets to produce frequent itemsets, analogize, producing frequent k- Itemsets. Its basic processes are: first scan the transaction database d Shi, 1- frequent itemsets. On this basis after connecting, trimmed 2- frequent itemsets. And so on until higher frequently cannot be set. K cycles, namely k- when frequency set, first k- candidate,k- candidate focused on each and every set is only one person on two different belonging to k-1 Frequent item sets the connection, k- Candidate set is filtered to produce k- Frequent item sets.

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Name Size Date
pinfanxiangji.m110.00 B30-10-13 10:16
apriori.m2.68 kB04-12-13 09:54
data.xlsx318.81 kB21-11-13 22:24
结果.txt8.40 kB04-12-13 10:57
<频繁项集>0.00 B04-12-13 10:55
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Data processing of frequent itemsets (93.94 kB)

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