Data Convexity and Parameter Independent Clustering for Biomedical Datasets

Md Anisur Rahman; Li-Minn Ang; Kah Phooi Seng

doi:10.1109/TCBB.2020.2978188

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Data Convexity and Parameter Independent Clustering for Biomedical Datasets

Journal article

Peer reviewed

Data Convexity and Parameter Independent Clustering for Biomedical Datasets

Md Anisur Rahman, Li-Minn Ang and Kah Phooi Seng

IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol.18(2), pp.765-772

2021

DOI: https://doi.org/10.1109/TCBB.2020.2978188

PMID: 32149694

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Abstract

Bioinformatics

biomedical

centroid-based clustering

density-based clustering

convexity

unique neighborhood set

In machine learning, the nature of the dataset itself such as convexity of the data point sets affects the right choice of clustering algorithm to give good performance. This brief paper first focuses on how data convexity influences the clustering performance on biomedical datasets. Then it addresses the main challenges of two well-known clustering groups which are centroid-based and density-based clustering. These techniques typically require a set of parameters to be provided by the user before the algorithms can perform well in terms of good clustering and give the optimal number of clusters. Two parameter independent clustering techniques utilizing unique neighborhood sets (UNSs) called Parameter Independent Convex Centroid-based Clustering (ConvexClust) for convex-dominated datasets and Parameter Independent Non-Convex Density-based Clustering (NonConvexClust) for nonconvex-dominated datasets are introduced. The ConvexClust and NonConvex Clust algorithms are extensively evaluated on real-world biomedical datasets. Their performances are also compared with other clustering algorithms using evaluation criteria such as SSE, entropy and purity. The results have revealed the good performance of the proposed parameter-independent clustering techniques and also shown that most of the biomedical datasets in the experiments demonstrated their tendency towards convex-dominated data point sets.

Details

Title: Data Convexity and Parameter Independent Clustering for Biomedical Datasets
Authors: Md Anisur Rahman (Author) - Charles Sturt University
Li-Minn Ang (Author) - University of the Sunshine Coast, Queensland, School of Science and Engineering - Legacy
Kah Phooi Seng (Author) - UNSW Australia
Publication details: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol.18(2), pp.765-772
Publisher: Institute of Electrical and Electronics Engineers
Date published: 2021
DOI: 10.1109/TCBB.2020.2978188
ISSN: 1557-9964; 1545-5963
PMID: 32149694
Organisation Unit: School of Science, Technology and Engineering; Engage Research Lab
Language: English
Record Identifier: 99526508402621
Output Type: Journal article

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Collaboration types: Domestic collaboration
Web Of Science research areas: Biochemical Research Methods; Computer Science, Interdisciplinary Applications; Mathematics, Interdisciplinary Applications; Statistics & Probability