Affiliation:
1. Engineering Department of Informatics Modelling Electronics and Systems Science, University of Calabria, 87036 Rende, Italy
2. CNR—National Research Council of Italy, Institute for High Performance Computing and Networking (ICAR), 87036 Rende, Italy
Abstract
K-Means is a “de facto” standard clustering algorithm due to its simplicity and efficiency. K-Means, though, strongly depends on the initialization of the centroids (seeding method) and often gets stuck in a local sub-optimal solution. K-Means, in fact, mainly acts as a local refiner of the centroids, and it is unable to move centroids all over the data space. Random Swap was defined to go beyond K-Means, and its modus operandi integrates K-Means in a global strategy of centroids management, which can often generate a clustering solution close to the global optimum. This paper proposes an approach which extends both K-Means and Random Swap and improves the clustering accuracy through an evolutionary technique and careful seeding. Two new algorithms are proposed: the Population-Based K-Means (PB-KM) and the Population-Based Random Swap (PB-RS). Both algorithms consist of two steps: first, a population of J candidate solutions is built, and then the candidate centroids are repeatedly recombined toward a final accurate solution. The paper motivates the design of PB-KM and PB-RS, outlines their current implementation in Java based on parallel streams, and demonstrates the achievable clustering accuracy using both synthetic and real-world datasets.
Subject
Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science
Reference36 articles.
1. Bell, J. (2020). Machine Learning: Hands-on for Developers and Technical Professionals, John Wiley & Sons.
2. Least squares quantization in PCM;Lloyd;IEEE Trans. Inf. Theory,1982
3. MacQueen, J. (1967). Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California.
4. Data clustering: 50 years beyond k-means;Jain;Pattern Recognit. Lett.,2010
5. K-means properties on six clustering benchmark datasets;Sieranoja;Appl. Intell.,2018
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献