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Error Evaluation on K- Means and Hierarchical Clustering with Effect of Distance Functions for Iris Dataset

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 86 - Number 16
Year of Publication: 2014
Authors:
Harish Kumar Sagar
Varsha Sharma
10.5120/15066-3429

Harish Kumar Sagar and Varsha Sharma. Article: Error Evaluation on K- Means and Hierarchical Clustering with Effect of Distance Functions for Iris Dataset. International Journal of Computer Applications 86(16):1-5, January 2014. Full text available. BibTeX

@article{key:article,
	author = {Harish Kumar Sagar and Varsha Sharma},
	title = {Article: Error Evaluation on K- Means and Hierarchical Clustering with Effect of Distance Functions for Iris Dataset},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {86},
	number = {16},
	pages = {1-5},
	month = {January},
	note = {Full text available}
}

Abstract

In Data clustering (a sub field of Data mining), k-means and hierarchical based clustering algorithms are popular due to its excellent performance in clustering of large data sets. This paper presents two different comparative studies which includes various Data Clustering algorithms for analyzing best one with minimum clustering error. The foremost objective of this paper is to divide the data objects into k number of different clusters with homogeneity and the each cluster should be heterogeneous to each other. However, these both algorithms (K-Mean and Hierarchical) are not free with the errors. In this paper, firstly various distance has been considered for these two algorithms for comparing and analyzing the best distance methods to solve the existing problems. .

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