Empirical Analysis on the Efficiency of Clustering Algorithms Based on the Significance of Cluster Size
Sunitha Cheriyan, Shaniba Ibrahim, Susan Treesa
Journal of Digital Information Management, Vol. 21, No. 1, 2023.
DOI: https://doi.org/10.6025/jdim/2023/21/1/9-17
Link: https://www.dline.info/fpaper/jdim/v21i1/jdimv21i1_2.pdf
Abstract:
This paper mainly focuses on the performance of the various clustering algorithm on a particular dataset based on the number of clusters defined. The analysis is performed on the iris dataset from the dataset library. It also compares the performance of the algorithms based on the number of clusters defined. The various algorithms used for the comparison includes K-Means, Hierarchical, Model based and Density based Clustering based on Statistical models.