JACIII Vol.19 No.5 pp. 655-661
doi: 10.20965/jaciii.2015.p0655


On Sequential Cluster Extraction Based on L1-Regularized Possibilistic c-Means

Yukihiro Hamasuna* and Yasunori Endo**

*Department of Informatics, School of Science and Engineering, Kindai University
3-4-1 Kowakae, Higashi-osaka, Osaka 577-8502, Japan

**Faculty of Engineering, Information and Systems, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

January 31, 2015
June 16, 2015
September 20, 2015
sequential cluster extraction, possibilistic clustering, L1-regularization, sequential hard c-means, classification function
Sequential cluster extraction algorithms are useful clustering methods that extract clusters one by one without the number of clusters having to be determined in advance. Typical examples of these algorithms are sequential hard c-means (SHCM) and possibilistic clustering (PCM) based algorithms. Two types of L1-regularized possibilistic clustering are proposed to induce crisp and possibilistic allocation rules and to construct a novel sequential cluster extraction algorithm. The relationship between the proposed method and SHCM is also discussed. The effectiveness of the proposed method is verified through numerical examples. Results show that the entropy-based method yields better results for the Rand Index and the number of extracted clusters.
Cite this article as:
Y. Hamasuna and Y. Endo, “On Sequential Cluster Extraction Based on L1-Regularized Possibilistic c-Means,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.5, pp. 655-661, 2015.
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