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A Survey on Unsupervised Clustering Algorithm based on K-Means Clustering

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 8
Year of Publication: 2016
Authors:
Yogiraj Singh Kushawah, Ashish Mohan Yadav
10.5120/ijca2016912481

Yogiraj Singh Kushawah and Ashish Mohan Yadav. A Survey on Unsupervised Clustering Algorithm based on K-Means Clustering. International Journal of Computer Applications 156(8):6-9, December 2016. BibTeX

@article{10.5120/ijca2016912481,
	author = {Yogiraj Singh Kushawah and Ashish Mohan Yadav},
	title = {A Survey on Unsupervised Clustering Algorithm based on K-Means Clustering},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {156},
	number = {8},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {6-9},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume156/number8/26727-2016912481},
	doi = {10.5120/ijca2016912481},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Data mining are data analysis supported unsupervised clustering algorithm is one of the quickest growing research areas because of availability of huge quantity of data analysis and extract usefully information based on new improve performance of clustering algorithm. Clustering is an unsupervised classification that's the partitioning of a data set in a set of meaningful subsets .Machine learning is based on extract and mine the invisible, meaningful data from mountain of data, hidden patterns the finding out clusters may be a supported unsupervised learning. K means is one of the best unsupervised learning strategies among all partitioning primarily based clustering strategies. The proposed algorithm is improving performance of clustering algorithm (IPCA) bases on experiment on various dataset. A proposed algorithm is minimizing error and optimization in cluster and also the effectiveness of the proposed clustering algorithm.

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Keywords

Clustering, K-means clustering cluster center, partitioning clustering, unsupervised learning.