A Parameter-free Affinity Based Clustering

TL;DR

This paper introduces a simple, parameter-free clustering method inspired by human cognition, which identifies closely lying points based on affinity to determine the number of clusters, demonstrating robustness to noise and density variations.

Contribution

A novel, parameter-free clustering approach that mimics human perception to identify clusters based on affinity, without requiring prior knowledge of the number of clusters.

Findings

Effective in identifying well-separated clusters even with outliers

Robust to noise and density variations in datasets

Performs well on large synthetic and real datasets

Abstract

Several methods have been proposed to estimate the number of clusters in a dataset; the basic ideal behind all of them has been to study an index that measures inter-cluster separation and intra-cluster cohesion over a range of cluster numbers and report the number which gives an optimum value of the index. In this paper we propose a simple, parameter free approach that is like human cognition to form clusters, where closely lying points are easily identified to form a cluster and total number of clusters are revealed. To identify closely lying points, affinity of two points is defined as a function of distance and a threshold affinity is identified, above which two points in a dataset are likely to be in the same cluster. Well separated clusters are identified even in the presence of outliers, whereas for not so well separated dataset, final number of clusters are estimated and the detected clusters are merged to produce the final clusters. Experiments performed with several large dimensional synthetic and real datasets show good results with robustness to noise and density variation within dataset.