Active Distance-Based Clustering using K-medoids

TL;DR

This paper introduces an active distance-based k-medoids clustering method that efficiently estimates unknown distances using a small subset, enabling effective clustering with fewer distance computations.

Contribution

The paper presents a novel active clustering algorithm that reduces the need for complete distance matrices by estimating unknown distances through triangle inequality and active querying.

Findings

Effective clustering with limited distance data

Reduces computational cost in large datasets

Performs well on real-world and synthetic data

Abstract

k-medoids algorithm is a partitional, centroid-based clustering algorithm which uses pairwise distances of data points and tries to directly decompose the dataset with $n$ points into a set of $k$ disjoint clusters. However, k-medoids itself requires all distances between data points that are not so easy to get in many applications. In this paper, we introduce a new method which requires only a small proportion of the whole set of distances and makes an effort to estimate an upper-bound for unknown distances using the inquired ones. This algorithm makes use of the triangle inequality to calculate an upper-bound estimation of the unknown distances. Our method is built upon a recursive approach to cluster objects and to choose some points actively from each bunch of data and acquire the distances between these prominent points from oracle. Experimental results show that the proposed method using only a small subset of the distances can find proper clustering on many real-world and synthetic datasets.