Analyzing the performance of various clustering algorithms

Analyzing the performance of various clustering algorithms

Автор: Bhupesh Rawat, Sanjay Kumar Dwivedi

Журнал: International Journal of Modern Education and Computer Science @ijmecs

Статья в выпуске: 1 vol.11, 2019 года.

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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.

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

Короткий адрес: https://sciup.org/15016824

IDR: 15016824   |   DOI: 10.5815/ijmecs.2019.01.06

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