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Optimization method of a frequent item set mining algorithm

A technology of frequent itemsets mining and optimization methods, applied in the field of data processing, can solve the problems that the project performance and cost cannot meet the requirements, frequent itemsets increase invalid frequent itemsets, increase the size of frequent itemsets, etc., so as to improve performance. and user experience, reduce the calculation process and, increase the effect of user experience

Active Publication Date: 2016-03-30
央视国际网络无锡有限公司
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  • Application Information

AI Technical Summary

Problems solved by technology

Although technologies such as hadoop, spark, and Fpgrowth can shorten the calculation time of frequent itemsets and the number of data iterations, data from different sources will increase the number of frequent itemsets and increase the number of invalid frequent itemsets.
The actual use effect in the project is not accurate, and the wrong result is often recommended
Moreover, the amount of invalid data will increase the size of frequent itemsets, so that the performance and cost of the project cannot meet the requirements

Method used

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  • Optimization method of a frequent item set mining algorithm
  • Optimization method of a frequent item set mining algorithm
  • Optimization method of a frequent item set mining algorithm

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Embodiment Construction

[0024] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0025] Such as figure 1 As shown, an optimization method of frequent itemsets mining algorithm, including:

[0026] Receive data;

[0027] For the received data, use preorder traversal to traverse the itemset tree to arrange the itemsets;

[0028] Compare the parent-subsets of the adjacent itemsets in the arranged itemsets, and merge the itemsets with the relationship between the true subset and the parent set as a result of the comparison;

[0029] Among them, itemset is the abbreviation of frequent itemsets.

[0030] Preferably, the parent-subset comparison includes the subordination relationship of the itemsets and the support of the itemsets.

[0031] Prefer...

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Abstract

The invention discloses an optimization method of a frequent item set mining algorithm. The method comprises the following steps of: for received data, using preorder traversal, traversing an item set tree, and thereby arranging item sets; executing parent set and subset comparison for neighboring item sets of the arranged item sets, and combining the item sets of which the comparison result is proper subsets and parent sets. As compared with the existing frequent item set mining algorithm, a function of extracting proper subsets is provide; and the method has main advantages of reducing size of data volume, reducing calculation process of data and reducing size of data storage through extraction of the proper subsets, and preventing repeated calculation of duplicated data through effective calculation for reducing invalid item sets.

Description

technical field [0001] The invention relates to the field of data processing, in particular to an optimization method for frequent item set mining algorithms. Background technique [0002] The frequent item set mining algorithm is used to mine item sets that often appear together (called frequent item sets). By mining these frequent item sets, when one item of the frequent item set appears in a transaction, the frequent item can be Set other items as recommendations. [0003] There are two common frequent itemset mining algorithms, one is the Apriori algorithm, and the other is the FPGrowth algorithm. FPGrowth is optimized based on the Apriori algorithm. Compared with Apriori, the biggest breakthrough of FPgrowth algorithm is to reduce the number of data iterations. Apriori needs to perform K-1 calculations to calculate frequent itemsets, K is the number of frequent itemsets, and Fpgrowth only needs to traverse the data twice to complete the calculation of frequent itemse...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/2453
Inventor 李磊
Owner 央视国际网络无锡有限公司
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