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Incremental Clustering using Genetic Algorithm and Particle Swarm Optimization

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Neha Chopade, Jitendra Sheetlani

Neha Chopade and Jitendra Sheetlani. Incremental Clustering using Genetic Algorithm and Particle Swarm Optimization. International Journal of Computer Applications 163(8):27-33, April 2017. BibTeX

	author = {Neha Chopade and Jitendra Sheetlani},
	title = {Incremental Clustering using Genetic Algorithm and Particle Swarm Optimization},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2017},
	volume = {163},
	number = {8},
	month = {Apr},
	year = {2017},
	issn = {0975-8887},
	pages = {27-33},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2017913674},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


There are many supervised clustering algorithms based on static datasets for finding their optimal clusters. Clustering is the task of organizing data into clusters such that the data objects that are similar to each other. For finding clusters of data stream of chunks, i.e. for dynamic clustering we proposed a incremental clustering algorithm which is a combination of genetic algorithm and particle swarm optimization. In this paper, first we convert diabetes dataset into rough sets by applying appropriate algorithm, then after conversion rough sets are taken as input for genetic algorithm and after processing fitted chromosomes are generated. These fitted chromosomes are taken as input for particle swarm optimization which results in producing optimized clusters without redundancy. In this paper results are also presented and their comparison from existing approach is also given.


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Data mining, PSO,ACO GA, fuzzy logic etc.