Analyzing the Performance of Various Clustering Algorithms

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Bhupesh Rawat 1,* Sanjay Kumar Dwivedi 1

1. BBAU/Department of Computer Science, Lucknow, 226025, India

* Corresponding author.


Received: 4 Nov. 2018 / Revised: 15 Nov. 2018 / Accepted: 25 Nov. 2018 / Published: 8 Jan. 2019

Index Terms

Cluster analysis, k-means algorithm, Hierarchical algorithm, Expectation maximization, Make density-based clustering, Agglomerative clustering, Divisive clustering, Birch, Cure


Clustering is one of the extensively used techniques in data mining to analyze a large dataset in order to discover useful and interesting patterns. It partitions a dataset into mutually disjoint groups of data in such a manner that the data points belonging to the same cluster are highly similar and those lying in different clusters are very dissimilar. Furthermore, among a large number of clustering algorithms, it becomes difficult for researchers to select a suitable clustering algorithm for their purpose. Keeping this in mind, this paper aims to perform a comparative analysis of various clustering algorithms such as k-means, expectation maximization, hierarchical clustering and make density-based clustering with respect to different parameters such as time taken to build a model, use of different dataset, size of dataset, normalized and un-normalized data in order to find the suitability of one over other.

Cite This Paper

Bhupesh Rawat, Sanjay Kumar Dwivedi, "Analyzing the Performance of Various Clustering Algorithms", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.1, pp. 45-53, 2019.DOI: 10.5815/ijmecs.2019.01.06


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