Cluster Validity Index for Big Data Based on Density Discriminant Analysis

Conference proceedings article

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Publication Details

Author list: Weerapong T., Sathapornvajana S., Padungweang P., Krathu W.

Publisher: Hindawi

Publication year: 2020

Start page: 1

End page: 4

Number of pages: 4

ISBN: 9781728181066

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097348238&doi=10.1109%2fIBDAP50342.2020.9245612&partnerID=40&md5=1161ba69d8859b5847d535dcf07efd70

Languages: English-Great Britain (EN-GB)

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Abstract

The important factor for clustering unsupervised data is the Cluster Validity Index indicating appropriate number of clusters. The paper proposes the application of the unsupervised density discriminant analysis algorithm for cluster validation in the context of Big Data. In particular, the experiment was conducted to perform clustering tasks on big dataset by using centroid based clustering algorithm and apply unsupervised density discriminant analysis algorithm to find the most appropriate number of clusters. The performance evaluation was performed by means of processing time. The result shows that the time used to perform the clustering task depends on number of features and clusters. © 2020 IEEE.

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