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

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Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
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.
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