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Investigating stochastic diffusion search in data clustering

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

Mohammad Majid Al-Rifaie, Daniel Joyce, Sukhi Shergill, Mark Bishop

Original languageEnglish
Title of host publicationIntelliSys 2015 - Proceedings of 2015 SAI Intelligent Systems Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages187-194
Number of pages8
ISBN (Print)9781467376068
DOIs
StatePublished - 18 Dec 2015
EventSAI Intelligent Systems Conference, IntelliSys 2015 - London, United Kingdom
Duration: 10 Nov 201511 Nov 2015

Conference

ConferenceSAI Intelligent Systems Conference, IntelliSys 2015
CountryUnited Kingdom
CityLondon
Period10/11/201511/11/2015

King's Authors

Abstract

The use of clustering in various applications is key to its popularity in data analysis and data mining. Algorithms used for optimisation can be extended to perform clustering on a dataset. In this paper, a swarm intelligence technique-Stochastic Diffusion Search-is deployed for clustering purposes. This algorithm has been used in the past as a multi-agent global search and optimisation technique. In the context of this paper, the algorithm is applied to a clustering problem, tested on the classical Iris dataset and its performance is contrasted against nine other clustering techniques. The outcome of the comparison highlights the promising and competitive performance of the proposed method in terms of the quality of the solutions and its robustness in classification. This paper serves as a proof of principle of the novel applicability of this algorithm in the field of data clustering.

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