Citation

Waqas, Syed Muhammad and Alim, Affan and Talpur, Kashif and Su’ud, Mazliham Mohd and Djenouri, Youcef and Ali, Syed Mubashir and Alam, Muhammad Mansoor (2026) A fuzzy membership-based enhancement to density peak clustering with comparative performance evaluation. Array, 30. p. 100785. ISSN 2590-0056

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Abstract

Clustering is a basic data mining operation that groups data points with similar inherent structure. Among clustering techniques, Density Peak Clustering (DPC) is notable for detecting clusters of arbitrary shapes without requiring the number of clusters in advance. However, DPC suffers from parameter-sensitive local density estimation, inadequate handling of noise and boundary points, and rigid binary cluster assignments. To overcome these limitations, fuzzy logic is often embedded in DPC, but the selection of the most appropriate membership function is still an open research question. In this paper, we propose an enhanced DPC variant with three methodological improvements: (i) substituting cutoff distance-based density estimation with a KNearest Neighbors (KNN) functioned kernel to achieve stable and robust density estimation, (ii) adding a noise parameter Lambda (

Item Type:	Article
Uncontrolled Keywords:	comparative analysis
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 May 2026 00:53
Last Modified:	07 May 2026 04:41
URII:	http://shdl.mmu.edu.my/id/eprint/15799

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