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Error Evaluation on K- Means and Hierarchical Clustering with Effect of Distance Functions for Iris Dataset
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 86 - Number 16 |
| Year of Publication: 2014 |
| Authors: Harish Kumar Sagar, Varsha Sharma |
10.5120/15066-3429
|
Harish Kumar Sagar, Varsha Sharma . Error Evaluation on K- Means and Hierarchical Clustering with Effect of Distance Functions for Iris Dataset. International Journal of Computer Applications. 86, 16 ( January 2014), 1-5. DOI=10.5120/15066-3429
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. .
References
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