A novel initialisation based on hospital-resident assignment for the k -modes algorithm

This paper presents a new way of selecting an initialisation for the k -modes algorithm that allows for a notion of game theoretic fairness that classic initialisations, namely those by Huang and Cao, do not. Our new method utilises the hospital-resident assignment problem to find the set of initial cluster centroids which we compare with two classical initialisation methods for k -modes: the original presented by Huang and the next most popular method of Cao and co-authors. To highlight the merits of our proposed method, two stages of analysis are presented. It is demonstrated that the proposed method is often able to offer computational speed-up of the order of 50% . Improved clustering, in terms of a commonly used cost-function, was witnessed in several cases and can be of the order of 10% , particularly for more complex datasets.