Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7682
Title: The Unification and Assessment of Multi-Objective Clustering Results of Categorical Datasets with H-Confidence Metric
Authors: Sert, Onur Can
Dursun, Kayhan
Özyer, Tansel
Jida, Jamal
Alhajj, Reda
Keywords: Multi-Objective Clustering
NSGA-II
h-confidence
Issue Date: 2012
Publisher: Graz Univ Technolgoy, Inst Information Systems Computer Media-Iicm
Abstract: Multi objective clustering is one focused area of multi objective optimization. Multi objective optimization attracted many researchers in several areas over a decade. Utilizing multi objective clustering mainly considers multiple objectives simultaneously and results with several natural clustering solutions. Obtained result set suggests different point of views for solving the clustering problem. This paper assumes all potential solutions belong to different experts and in overall; ensemble of solutions finally has been utilized for finding the final natural clustering. We have tested on categorical datasets and compared them against single objective clustering result in terms of purity and distance measure of k-modes clustering. Our clustering results have been assessed to find the most natural clustering. Our results get hold of existing classes decided by human experts.
URI: https://hdl.handle.net/20.500.11851/7682
ISSN: 0948-695X
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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