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
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.
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