Clustering biomedical and gene expression datasets with kernel density and unique neighborhood set based vein detection

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

doi:10.1016/j.is.2020.101490

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Clustering biomedical and gene expression datasets with kernel density and unique neighborhood set based vein detection

Journal article

Peer reviewed

Clustering biomedical and gene expression datasets with kernel density and unique neighborhood set based vein detection

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

Information Systems, Vol.91, 101490

2020

DOI: https://doi.org/10.1016/j.is.2020.101490

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Abstract

clustering

biomedical

gene expression

kernel density estimation

vein-based clustering

unique neighborhood set

It is a crucial need for a clustering technique to produce high-quality clusters from biomedical and gene expression datasets without requiring any user inputs. Therefore, in this paper we present a clustering technique called KUVClust that produces high-quality clusters when applied on biomedical and gene expression datasets without requiring any user inputs. The KUVClust algorithm uses three concepts namely multivariate kernel density estimation, unique closest neighborhood set and vein-based clustering. Although these concepts are known in the literature, KUVClust combines the concepts in a novel manner to achieve high-quality clustering results. The performance of KUVClust is compared with established clustering techniques on real-world biomedical and gene expression datasets. The comparisons were evaluated in terms of three criteria (purity, entropy, and sum of squared error (SSE)). Experimental results demonstrated the superiority of the proposed technique over the existing techniques for clustering both the low dimensional biomedical and high dimensional gene expressions datasets used in the experiments.

Details

Title: Clustering biomedical and gene expression datasets with kernel density and unique neighborhood set based vein detection
Authors: Md Anisur Rahman (Author) - Charles Sturt University
Li-Minn Ang (Author) - University of the Sunshine Coast - School of Science and Engineering
Kah Phooi Seng (Author) - University of New South Wales
Publication details: Information Systems, Vol.91, 101490
Publisher: Elsevier Ltd.
Date published: 2020
DOI: 10.1016/j.is.2020.101490
ISSN: 0306-4379
Organisation Unit: School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
Language: English
Record Identifier: 99450971302621
Output Type: Journal article

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Collaboration types: Domestic collaboration
Web Of Science research areas: Computer Science, Information Systems