Int. J. of Data Mining, Modelling and Management   »   2017 Vol.9, No.4

 

 

Title: GACC: genetic algorithm-based categorical data clustering for large datasets

 

Authors: Abha Sharma; R.S. Thakur

 

Addresses:
Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India
Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India

 

Abstract: Many operators of genetic algorithm (GA) are discussed in the literature such as crossover operators, fitness functions, mutation, etc. A range of GA-based clustering methods have been proposed to obtain optimal solutions. In this paper, most recent GA-based hard and fuzzy clustering which is specifically designed for categorical data is discussed. In general, all GA-based clustering algorithms generate the initial population randomly, which may produce biased results. This paper proposed GACC algorithm with new population initialisation criteria. In this population creation mechanism, the usual random selection of chromosomes is replaced with more refined and distinct clusters as chromosomes. This mechanism prohibits the user to initialise the population size as well. Experimental results show the better clustering for the pure categorical dataset. The work finishes off with some open challenges and ways to improve clustering of categorical data.

 

Keywords: clustering; categorical data; genetic algorithm; genetic operators; initial population; population size.

 

DOI: 10.1504/IJDMMM.2017.10009451

 

Int. J. of Data Mining, Modelling and Management, 2017 Vol.9, No.4, pp.275 - 297

 

Date of acceptance: 23 Feb 2017
Available online: 01 Dec 2017

 

 

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