MCOKE: Multi-Cluster Overlapping K-Means Extension Algorithm

Date

2015

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

DOI