MCOKE: Multi-Cluster Overlapping K-Means Extension Algorithm

Said Baadel, Fadi Thabtah, Zhongyu Lu

Research output: Contribution to journalArticle


Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard-partitioning techniques where each object is assigned to one cluster. In this paper we propose an overlapping algorithm MCOKE which allows objects to belong to one or more clusters. The algorithm is different from fuzzy clustering techniques because objects that overlap are assigned a membership value of 1 (one) as opposed to a fuzzy membership degree. The algorithm is also different from other overlapping algorithms that require a similarity threshold be defined a priori which can be difficult to determine by novice users.
Original languageEnglish
Article number104
Pages (from-to)427-430
Number of pages4
JournalInternational Journal of Computer, Electrical, Automation, Control and Information Engineering
Issue number2
Publication statusPublished - 2015


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