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K-means with Three different Distance Metrics

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International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 67 - Number 10
Year of Publication: 2013
Authors:
Archana Singh
Avantika Yadav
Ajay Rana
10.5120/11430-6785

Archana Singh, Avantika Yadav and Ajay Rana. Article: K-means with Three different Distance Metrics. International Journal of Computer Applications 67(10):13-17, April 2013. Full text available. BibTeX

@article{key:article,
	author = {Archana Singh and Avantika Yadav and Ajay Rana},
	title = {Article: K-means with Three different Distance Metrics},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {67},
	number = {10},
	pages = {13-17},
	month = {April},
	note = {Full text available}
}

Abstract

The power of k-means algorithm is due to its computational efficiency and the nature of ease at which it can be used. Distance metrics are used to find similar data objects that lead to develop robust algorithms for the data mining functionalities such as classification and clustering. In this paper, the results obtained by implementing the k-means algorithm using three different metrics Euclidean, Manhattan and Minkowski distance metrics along with the comparative study of results of basic k-means algorithm which is implemented through Euclidian distance metric for two-dimensional data, are discussed. Results are displayed with the help of histograms.

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