A Review of Clustering Methods forming Non-Convex clusters with, Missing and Noisy Data

Authors

  • Bhargav S School of Information Technology, RGPV, India
  • Pawar M Department of IT, UIT, RGPV, India

Keywords:

Clustering, convex, non-convex, missing values, Big Data, noisy data, data mining, density based

Abstract

Clustering problem is among the foremost quests in Machine Learning Paradigm. The Big Data sets, being versatile, multisourced & multivariate, could have noise, missing values, & may form clusters with arbitrary shape. Because of unpredictable nature of Big Data Sets, the clustering method should be able to handle missing values, noise, & should be able to make arbitrary shaped clusters. The partition based methods for clustering does not form non-convex clusters, The Hierarchical Clustering Methods & Algorithms are able to make arbitrary shaped clusters but they are not suitable for large data set due to time & computational complexity. Density & Grid Paradigm do not solve the issue related to missing values. Combining different Clustering Methods could eradicate the mutual issues they have pertaining to dataset’s geometrical and spatial properties, like missing data, non-convex shapes, noise etc.

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Published

2025-11-11

How to Cite

[1]
S. Bhargav and M. Pawar, “A Review of Clustering Methods forming Non-Convex clusters with, Missing and Noisy Data”, Int. J. Comp. Sci. Eng., vol. 4, no. 3, pp. 39–44, Nov. 2025.

Issue

Vol. 4 No. 3 (2016): IJCSE March Edition

Section

Review Article

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