TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Approximate k values using Repulsive Force without Domain Knowledge in k-means |
| 영문제목(English Title) |
Approximate k values using Repulsive Force without Domain Knowledge in k-means |
| 저자(Author) |
Jung-Jae Kim
Minwoo Ryu
and Si-Ho Cha
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| 원문수록처(Citation) |
VOL 14 NO. 03 PP. 0976 ~ 0990 (2020. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The k-means algorithm is widely used in academia and industry due to easy and simple implementation, enabling fast learning for complex datasets. However, k-means struggles to classify datasets without prior knowledge of specific domains. We proposed the repulsive k-means (RK-means) algorithm in a previous study to improve the k-means algorithm, using the repulsive force concept, which allows deleting unnecessary cluster centroids. Accordingly, the RK-means enables to classifying of a dataset without domain knowledge. However, three main problems remain. The RK-means algorithm includes a cluster repulsive force offset, for clusters confined in other clusters, which can cause cluster locking; we were unable to prove RK-means provided optimal convergence in the previous study; and RK-means shown better performance only normalize term and weight. Therefore, this paper proposes the advanced RK-means (ARK-means) algorithm to resolve the RK-means problems. We establish an initialization strategy for deploying cluster centroids and define a metric for the ARK-means algorithm. Finally, we redefine the mass and normalize terms to close to the general dataset. We show ARK-means feasibility experimentally using blob and iris datasets. Experiment results verify the proposed ARK-means algorithm provides better performance than k-means, k’-means, and RK-means.
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| 키워드(Keyword) |
Clustering
k-means algorithm
machine learning
repulsive force
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| 파일첨부 |
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