JACIII Vol.11 No.9 pp. 1136-1143
doi: 10.20965/jaciii.2007.p1136


Arbitrary-Shaped Cluster Separation Using One-Dimensional Data Mapping and Histogram Segmentation

Seiji Hotta*, Senya Kiyasu**, and Sueharu Miyahara**

*Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan

**Department of Computer and Information Sciences, Nagasaki University, 1-14 Bunkyo-machi, Nagasaki-shi, Nagasaki 852-8521, Japan

October 2, 2006
August 16, 2007
November 20, 2007
clustering, arbitrarily shaped cluster, one-dimensional data mapping, histogram segmentation, discriminant threshold selection
Of the many clustering methods proposed for separating arbitrarily shaped clusters, most had drawbacks in parameter sensitivity and high-computational cost requiring large amounts of memory. We propose one-dimensional (1D) mapping for separating arbitrarily shaped clusters using a list of neighbors. After mapping, we apply a discriminant threshold selection to the histogram of the data distribution in 1D space. We verified the feasibility of performance in experiments on synthetic toy data, image, and video segmentation.
Cite this article as:
S. Hotta, S. Kiyasu, and S. Miyahara, “Arbitrary-Shaped Cluster Separation Using One-Dimensional Data Mapping and Histogram Segmentation,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.9, pp. 1136-1143, 2007.
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