Title: HCFPC: a new hybrid clustering framework using partition-based clustering algorithms to group functionally similar genes from microarray gene expression data

Authors: Shilpi Bose; Chandra Das; Aishwariya Barik; Kuntal Ghosh; Matangini Chattopadhyay; Samiran Chattopadhyay

Addresses: Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, West Bengal, India ' Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, West Bengal, India ' Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, West Bengal, India ' Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India ' School of Education Technology, Jadavpur University, Kolkata, West Bengal, India ' Techno India University, EM-4, Sector V, Salt Lake City, Kolkata, West Bengal, India

Abstract: Clustering of genes from microarray gene expression data is one of the important analysis tasks as it has a great impact on the functionality prediction of novel genes and also in gene regulatory network formation. Partition-based clustering algorithms are the most popular and most of the time give consistent results but these methods cannot handle noise points. Apart from this, hard-k-means cannot handle overlapping data. Although fuzzy-k-means and possibilistic-k-means can handle overlapping data, these methods have several shortcomings related to their membership function. In this regard, here, a hybrid clustering framework using different partition-based clustering algorithms (hard-k-means, fuzzy-k-means, and possibilistic-k-means) named HCFPC is proposed to find clusters from overlapping objects via eliminating noise from highly noisy datasets. It also can minimise different drawbacks that appear in fuzzy and possibilistic approaches. To validate this method, it is applied to highly noisy gene expression datasets to find clusters from overlapping genes via eliminating noise. Experimental results show the efficiency of the proposed hybrid framework.

Keywords: clustering; k-means; fuzzy-k-means; possibilistic k-means; microarray technology; gene expression data.

DOI: 10.1504/IJBRA.2026.151284

International Journal of Bioinformatics Research and Applications, 2026 Vol.22 No.1, pp.68 - 94

Received: 19 Oct 2023
Accepted: 07 Apr 2024
Published online: 22 Jan 2026 *

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