MCOKE: Multi-Cluster Overlapping K-Means Extension Algorithm

Date
2015
Authors
Baadel, Said
Thabtah, Fadi
Lu, Joan
Journal Title
Journal ISSN
Volume Title
Publisher
World Academy of Science, Engineering and Technology
Abstract
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.
Description
This article is not available at CUD collection. The version of scholarly record of this article is published in World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering (2020), available online at: https://publications.waset.org/10000529/pdf
Keywords
Data mining, k-means, MCOKE, overlapping
Citation
Baadel, S., Thabtah, F., Lu, J. (2015). MCOKE: Multi-Cluster Overlapping K-Means Extension Algorithm. World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering, 9(2), 427 - 430. https://publications.waset.org/10000529/pdf