Overlapping clustering: A review

Said Baadel; Fadi Thabtah; Joan Lu

doi:10.1109/SAI.2016.7555988

Overlapping clustering: A review

Said Baadel, Fadi Thabtah, Joan Lu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

31 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 language	English
Title of host publication	Proceedings of 2016 SAI Computing Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	233-237
Number of pages	5
ISBN (Electronic)	9781467384605, 9781467384599
DOIs	https://doi.org/10.1109/SAI.2016.7555988
Publication status	Published - 1 Sep 2016
Event	Science and Information Conferences Computing Conference 2016 - London, United Kingdom Duration: 13 Jul 2016 → 15 Jul 2016 http://saiconference.com/Conferences/Computing2016 (Link to Conference Website)

Conference

Conference	Science and Information Conferences Computing Conference 2016
Abbreviated title	SAI 2016
Country/Territory	United Kingdom
City	London
Period	13/07/16 → 15/07/16
Internet address	http://saiconference.com/Conferences/Computing2016 (Link to Conference Website)

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10.1109/SAI.2016.7555988Licence: Other

http://eprints.hud.ac.uk/id/eprint/29325/Licence: Unspecified

Cite this

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title = "Overlapping clustering: A review",

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.",

keywords = "clustering, Data mining, k-means, MCOKE, overlapping clustering",

author = "Said Baadel and Fadi Thabtah and Joan Lu",

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N2 - 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.

AB - 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.

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BT - Proceedings of 2016 SAI Computing Conference

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Overlapping clustering: A review

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