K-sets and k-swaps algorithms for clustering sets. (July 2023)

• Record Type: Journal Article
• Authors: Rezaei, Mohammad; Fränti, Pasi
• Is Part Of: Pattern recognition. Volume 139(2023)
• Publication Date: 2023-07
• Subjects: Clustering sets -- Similarity of sets -- K-means -- K-medoids -- Random swap -- K-swaps -- Customer segmentation -- Clustering healthcare records; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2023.109454
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Novel K-sets algorithm that generalizes k-means to work for sets data. Novel K-swaps algorithm to avoid local minima. Benchmark for evaluating clustering of sets data. Case study with clustering patients based on their ICD-10 diagnoses. Abstract: We present two new clustering algorithms called k-sets and k-swaps for data where each object is a set. First, we define the mean of the sets in a cluster, and the distance between a set and the mean. We then derive the k-sets algorithm from the principles of classical k -means so that it repeats the assignment and update steps until convergence. To the best of our knowledge, the proposed algorithm is the first k -means based algorithm for this kind of data. We adopt the idea also into random swap algorithm, which is a wrapper around the k -means that avoids local minima. This variant is called k-swaps . We show by experiments that this algorithm provides more accurate clustering results than k-medoids and other competitive methods.