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A hybrid GeneticMax algorithm for improving the traditional genetic based approach for mining maximal frequent item sets


Kabir, MMJ and Xu, S and Kang, BH and Zhao, Z, A hybrid GeneticMax algorithm for improving the traditional genetic based approach for mining maximal frequent item sets, International Journal of Computer Science and Network Security, 14, (10) pp. 27-35. ISSN 1738-7906 (2014) [Refereed Article]

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Copyright 2014 IJCSNS

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Mining Frequent item sets is one of the most useful data mining methods which discovers important relationships among attributes of data sets. Initially it was developed for market basket analysis, but these days it is used to solve any task where discovering hidden relationships among different attributes is required. Mining frequent item sets plays a vital role for generating association rules, finding correlations and many more interesting relationships among different sort of data. A major challenge in the frequent item set mining task is that it generates a huge number of frequent sub item sets from dense data sets. Researchers proposed mining maximal frequent item sets to overcome this problem. Maximal frequent item sets contain the information of an exponential number of frequent sub item sets since if an item set is frequent each of its sub item sets is also frequent. Very few studies have applied evolutionary algorithms to mine maximal frequent item sets using thorough experimental analysis. In a previous study, we showed the efficiency of using a genetic based approach named GeneticMax to find maximal frequent item sets. In this study we will introduce a new algorithm name, hybrid GeneticMax, which uses local search along with a genetic algorithm to mine maximal frequent item sets from large data sets. The purpose of using the genetic algorithm is that this algorithm based approach is robust and the existing genetic based method which is working fine for a specific problem can be improved by hybridizing it. Experiments are performed on different real world data sets as well as on a synthetic data set. Our new scheme compared favorably to existing GeneticMax under certain conditions which are being evaluated.

Item Details

Item Type:Refereed Article
Keywords:association rule mining, maximal frequent item sets, genetic algorithm, lexicographic tree, data mining
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Kabir, MMJ (Mr Mir Kabir)
UTAS Author:Xu, S (Dr Shuxiang Xu)
UTAS Author:Kang, BH (Professor Byeong Kang)
UTAS Author:Zhao, Z (Dr Zongyuan Zhao)
ID Code:96660
Year Published:2014
Deposited By:Information and Communication Technology
Deposited On:2014-11-14
Last Modified:2018-03-18

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