Joint Feature Learning and Clustering Techniques for Clustering High Dimensional Data: A Review

Authors

  • Ghatage Trupti B Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, Kolhapur, Maharashtra, India
  • Patil Deepali E Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, Kolhapur, Maharashtra, India
  • Takmare Sachin B Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, Kolhapur, Maharashtra, India
  • Patil Sushama A DC Branch, Dept of Digital Communication, SSSIST Sehore.

Keywords:

Clustering, high dimensional data, feature learning, dimensionality reduction

Abstract

In many real world applications, we often face high dimensional data. Developing efficient clustering methods for high dimensional datasets may be a challenging problem because of the curse of dimensionality. Common method to deal with this is to use first dimensionality reduction approach and then cluster the data in the lower dimensions. Even though we can initially reduce the dimensionality by any approach and then use clustering approaches to group high dimensional data, performance can also be improved since these two techniques are conducted in sequence. Naturally, if we consider the requirement of clustering during the process of dimensionality reduction and vice versus then the performance of clustering can be improved. This paper presents a review of different techniques for clustering high dimensional data by joint feature learning and clustering.

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Published

2025-11-11

How to Cite

[1]
B. Ghatage Trupti, E. Patil Deepali, B. Takmare Sachin, and A. Patil Sushama, “Joint Feature Learning and Clustering Techniques for Clustering High Dimensional Data: A Review”, Int. J. Comp. Sci. Eng., vol. 4, no. 3, pp. 54–58, Nov. 2025.

Issue

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

Section

Review Article

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