Careful seeding for the k-medoids algorithm with incremental k++ cluster construction

• URL: http://arxiv.org/abs/2207.02404v1
• Date: Wed, 6 Jul 2022 02:25:35 GMT
• Title: Careful seeding for the k-medoids algorithm with incremental k++ cluster construction
• Authors: Difei Cheng, Bo Zhang
• Abstract summary: An improved k-medoids algorithm (INCKM) was recently proposed to overcome this drawback. We propose a novel incremental k-medoids algorithm (INCKPP) which dynamically increases the number of clusters. Our algorithm can overcome the parameter selection problem in the improved k-medoids algorithm, improve clustering performance, and deal with imbalanced datasets very well.
• Score: 4.981260380070016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The k-medoids algorithm is a popular variant of the k-means algorithm and widely used in pattern recognition and machine learning. A main drawback of the k-medoids algorithm is that it can be trapped in local optima. An improved k-medoids algorithm (INCKM) was recently proposed to overcome this drawback, based on constructing a candidate medoids subset with a parameter choosing procedure, but it may fail when dealing with imbalanced datasets. In this paper, we propose a novel incremental k-medoids algorithm (INCKPP) which dynamically increases the number of clusters from 2 to k through a nonparametric and stochastic k-means++ search procedure. Our algorithm can overcome the parameter selection problem in the improved k-medoids algorithm, improve the clustering performance, and deal with imbalanced datasets very well. But our algorithm has a weakness in computation efficiency. To address this issue, we propose a fast INCKPP algorithm (called INCKPP$_{sample}$) which preserves the computational efficiency of the simple and fast k-medoids algorithm with an improved clustering performance. The proposed algorithm is compared with three state-of-the-art algorithms: the improved k-medoids algorithm (INCKM), the simple and fast k-medoids algorithm (FKM) and the k-means++ algorithm (KPP). Extensive experiments on both synthetic and real world datasets including imbalanced datasets illustrate the effectiveness of the proposed algorithm.

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