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Threshold Computation to Discover Cluster Structure: A New Approach

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 6 No. 1 (2016)

DOI
10.11591/ijece.v6i1.7381
ISSN
2088-8708, 2088-8708
Published
2016-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc-nd/4.0

Abstract

Cluster members are decided based on how close they are with each other. Compactness of cluster plays an important role in forming better quality clusters. ICNBCF incremental clustering algorithm computes closeness factor between every two data series. To decide members of cluster, it is necessary to know one more decisive factor to compare, threshold. Internal evaluation measure of cluster like variance and dunn index provide required decisive factor. in intial phase of ICNBCF, this decisive factor was given manually by investigative formed closeness factors. With values generated by internal evaluation measure formule, this process can be automated. This paper shows the detailed study of various evaluation measuress to work with new incremental clustreing algorithm ICNBCF.

Collaboration network of the authors

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Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 1 Nov 2026.
Metadata as oai_dc, converted from Crossref 
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