Imbalanced Data Clustering using Equilibrium K-Means

arXiv:2402.14490v1 Announce Type: new
Abstract: Imbalanced data, characterized by an unequal distribution of data points across different clusters, poses a challenge for traditional hard and fuzzy clustering algorithms, such as hard K-means (HKM, or Lloyd's algorithm) and fuzzy K-means (FKM, or Bezdek's algorithm). This paper introduces equilibrium K-means (EKM), a novel and simple K-means-type algorithm that alternates between just two steps, yielding significantly improved clustering results for imbalanced data by reducing the tendency of centroids to crowd together in the …

abstract algorithm algorithms arxiv challenge clustering cs.lg data distribution equilibrium k-means novel paper simple stat.ml type