Community discovery algorithm based on improved association rule

Abstract

The invention discloses a community discovery algorithm based on an improved association rule, and the algorithm comprises the steps: firstly carrying out the self-adaption of a support degree, and calculating the minimum support degree through a mathematic method; secondly, introducing a Boolean matrix and a transaction weight thought to improve an Apriori algorithm, and reducing the database scanning frequency; and finally, combining with a Spark platform to realize association rule improved community discovery algorithm parallelization. According to the community discovery algorithm based on the improved association rule, the community members are mined by using the MAC address. The Apriori algorithm is improved by introducing the idea of support degree self-adaption and adding a transaction weight to generate a Boolean matrix, the improved algorithm is combined with Spark to realize parallelization of the algorithm, and the relationship between community members is mined by mininga frequent item set. Experimental results show that the ARCD algorithm solves the problems of subjectivity of manual setting of support degree and redundancy of community mining results, has good expandability, and improves the mining speed of community discovery.

Description

technical field [0001] The invention relates to a community discovery algorithm based on improved association rules. Background technique [0002] With the advent of the era of big data, the construction of wireless cities has gradually improved. Complex network research has always been a hot spot in social research, and community discovery plays an important role in the study of complex networks. It is a new challenge to mine the community relations existing in wireless cities in massive data. [0003] Aiming at the traditional community discovery algorithm, a hybrid algorithm that combines the community discovery algorithm and association rules is proposed. The improved algorithm improves the accuracy of community discovery, but the improved algorithm introduces the shortcomings of the association rule algorithm, which increases the search time and reduces the search efficiency. Ma Wei et al. proposed to use directed unweighted graphs to improve the CS algorithm for com...

AI Technical Summary

Problems solved by technology

Zhang Yan et al. proposed to use the binary tree structure to improve the community discovery algorithm, and combine MapReduce and binary tree to improve the community discovery algorithm, realize the parallelization of the algorithm, and solve the problems of low efficiency and data overflow when processing massive data, but when MapReduce iterates , need to scan the disk frequently, increase the calculation time

Yang Qinliu et al. proposed to use matrix to improve association rules. This algorithm solves the shortcomings of traditional association rule algorithms that frequently scan transaction data sets and improves operational efficiency. However, this algorithm consumes a lot of time when processing massive data.

Wang Xueping put forward the idea of ​​self-adaptive support degree and confidence degree of Apriori algorithm. This algorithm solves the problems of subjectivity and no scientific basis when artificially setting support degree and confidence degree, but the algorithm does not solve the shortcomings of traditional Apriori algorithm.

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