Open AccessThreshold Computation to Discover Cluster Structure: A New Approach
Author(s) -
Preeti Mulay
Publication year - 2016
Publication title -
international journal of electrical and computer engineering (ijece)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v6i1.pp275-282
Subject(s) - closeness , cluster (spacecraft) , cluster analysis , computer science , measure (data warehouse) , factor (programming language) , variance (accounting) , data mining , computation , quality (philosophy) , process (computing) , algorithm , artificial intelligence , mathematics , epistemology , accounting , business , mathematical analysis , programming language , operating system , philosophy
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.
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