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Publication
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Research Title |
Detecting cluster numbers based on density changes using density-index enhanced Scale-invariant density-based clustering initialization algorithm |
Date of Distribution |
4 September 2017 |
Conference |
Title of the Conference |
The 9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017 |
Organiser |
Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang (KMITL), Thailand |
Conference Place |
Centara Grand Beach Resort Phuket |
Province/State |
ภูเก็ต |
Conference Date |
12 October 2017 |
To |
13 October 2017 |
Proceeding Paper |
Volume |
9 |
Issue |
9 |
Page |
288-292 |
Editors/edition/publisher |
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Abstract |
Despite high accuracy, K-means relies mainly on the determination of the suitable number of clusters. To cope with, it is hypothesized that in a dataset region with high density tends to be a cluster. The present study is based on Scale-invariant density-based clustering initialization, in which a cluster numbers is derived from density change analysis or density distribution analysis. However, the density calculation under this approach is based on the number and volume of data, which may result in inaccuracy for cluster detection. Thus, the objective of this study was to improve the performance of Scale-invariant density-based clustering initialization to detect the appropriate cluster numbers and initial cluster centers. We proposed a density calculation based on data distance. The density value obtained from the calculation was used as a condition of data division and data merging for cluster detection. According to the experiment, compared to the Scale invariant density-based clustering initialization, the proposed method could detect the cluster numbers and initial cluster centers more equal or closer to the actual number of clusters. In addition, the level of accuracy in clustering was higher than its counterpart. |
Author |
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Peer Review Status |
ไม่มีผู้ประเมินอิสระ |
Level of Conference |
นานาชาติ |
Type of Proceeding |
Full paper |
Type of Presentation |
Oral |
Part of thesis |
false |
Presentation awarding |
false |
Attach file |
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Citation |
0
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