Name
Title | ||
|---|---|---|
Arbitrary-Shaped Cluster Separation Using One-Dimensional Data Mapping And Histogram Segmentation |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.20965/jaciii.2007.p1136 | JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS |
Keywords | Field | DocType |
clustering, arbitrarily shaped cluster, one-dimensional data mapping, histogram segmentation, discriminant threshold selection | Histogram,Scale-space segmentation,Pattern recognition,Data mapping,Computer science,Segmentation,Histogram matching,Artificial intelligence,Region growing,Image histogram,Cluster analysis,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 9 | 1343-0130 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seiji Hotta | 1 | 6 | 4.98 |
| Senya Kiyasu | 2 | 4 | 2.10 |
| Sueharu Miyahara | 3 | 40 | 7.47 |