Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing

2024/01/21 by Sunil Aryal, Aryal, Sunil, Jonathan R. Wells +5
Computer Science · Medicine · Physics and Astronomy · #Advanced Clustering Algorithms Research #Complex Network Analysis Techniques #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2401.11402

openalex publication_date 2024/01/21 · openalex created_date 2024/01/24 · openalex updated_date 2026/07/28

Abstract

In this paper, we show that preprocessing data using a variant of rank transformation called 'Average Rank over an Ensemble of Sub-samples (ARES)' makes clustering algorithms robust to data representation and enable them to detect varying density clusters. Our empirical results, obtained using three most widely used clustering algorithms-namely KMeans, DBSCAN, and DP (Density Peak)-across a wide range of real-world datasets, show that clustering after ARES transformation produces better and more consistent results.

Related

• VDPC: Variational Density Peak Clustering Algorithm
• Persistent Multiscale Density-based Clustering
• An Algorithmic Introduction to Clustering
• SACA: Selective Attention-Based Clustering Algorithm
• Revisiting Agglomerative Clustering