Overlapping clustering: A review

Said Baadel, Fadi Thabtah, Joan Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

Data Clustering or unsupervised classification is one of the main research area in Data Mining. Partitioning Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard (crisp) partitioning techniques where each object is assigned to one cluster. Other algorithms utilise overlapping techniques where an object may belong to one or more clusters. Partitioning algorithms that overlap include the commonly used Fuzzy K-means and its variations. Other more recent algorithms reviewed in this paper are: the Overlapping K-Means (OKM), Weighted OKM (WOKM), the Overlapping Partitioning Cluster (OPC), and the Multi-Cluster Overlapping K-means Extension (MCOKE). This review focuses on the above mentioned partitioning methods and future direction in overlapping clustering is highlighted in this paper.

Original languageEnglish
Title of host publicationProceedings of 2016 SAI Computing Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages233-237
Number of pages5
ISBN (Electronic)9781467384605, 9781467384599
DOIs
Publication statusPublished - 1 Sep 2016
EventScience and Information Conferences Computing Conference 2016 - London, United Kingdom
Duration: 13 Jul 201615 Jul 2016
http://saiconference.com/Conferences/Computing2016 (Link to Conference Website)

Conference

ConferenceScience and Information Conferences Computing Conference 2016
Abbreviated titleSAI 2016
CountryUnited Kingdom
CityLondon
Period13/07/1615/07/16
Internet address

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  • Cite this

    Baadel, S., Thabtah, F., & Lu, J. (2016). Overlapping clustering: A review. In Proceedings of 2016 SAI Computing Conference (pp. 233-237). [7555988] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SAI.2016.7555988