Abstract
Clustering is a standard paradigm for associating some similar objects to a cluster while associating others to their respective clusters in an unsupervised manner. Different techniques are proposed in the domain of clustering depending upon the underlying methodology slightly vary from one another. Some of these are regarded as Hierarchical Clustering, K-means Clustering, Spectral Clustering, Affinity Propagation and Density based spatial Clustering. Their strengths and weaknesses are different for different datasets. In this research, we have used three benchmark datasets to analyze the merits and demerits for each of the clustering techniques. After applying these algorithms to the datasets named letter, glass and wine, we presented the critical review on why a specific clustering algorithm lacks with respect to execution time and clustering quality measured by Silhouette’s coefficient and why an algorithm performs better than others on the same dataset.
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This research was supported by Universiti Tun Hussein Onn Malaysia (UTHM) through Tier 1 vot. H938.
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Amna, Nawi, N.M., Aamir, M., Mushtaq, M.F. (2022). The Comparative Performance Analysis of Clustering Algorithms. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_34
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