Frequent item set mining method for game prop recommendation

Abstract

The invention discloses a frequent item set mining method for game prop recommendation and belongs to the technical field of data mining. The frequent item set mining method comprises the following steps: firstly, acquiring occurrence times of each item on MapReduce, performing sequencing and threshold screening to reject unqualified items and obtain F-List, dividing the F-List so as to obtain G-List, according to division of G-List, transmitting data to Mapper, performing Mapper processing, transmitting data to Reducer, and performing MapReduce mining on the Reducer; and for mining, firstly acquiring PPCTree on each Reducer, after PPCTree is obtained, acquiring L-List and G-Subsume of corresponding items on each Reducer, and finally performing recursion according to the N-List and the G-Subsume, thereby obtaining a final frequent item set. By adopting the frequent item set mining method, the data is reasonably divided according to load prediction, and thus load balance can be ensured; and as the recursion mining process is optimized, the dense data mining time can be greatly shortened.

Description

technical field [0001] The invention belongs to the field of data mining, and more specifically relates to a frequent item set mining method. Background technique [0002] Since the birth of data mining technology, it has been committed to discovering valuable information hidden in data. There are six modes of data mining: classification mode, clustering mode, regression mode, association mode, sequence mode and deviation mode. Among them, the association model analysis is an important research direction. Frequent itemset mining is an important part of association rule mining algorithms. Useful rules can be found in big data through the frequent itemset mining algorithm. This method can be applied in many fields, such as web log mining, commercial sales, and financial industry to recommend financial services that they may be interested in for different types of customer groups. Business and game application prop recommendation, etc. However, in the context of big data, th...

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Problems solved by technology

[0007] Aiming at the defects or urgent technical demands of the prior art, the present invention discloses a parallel frequent item set mining method on the MapReduce platform, reasonably divides data according to load prediction, and ensures load balance; by optimizing the recursive mining process, greatly reducing intensive Data mining time, which solves the problems of low algorithm efficiency and unbalanced load

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