NDLI: Cooperative DynDE for temporal data clustering

Content Provider	IEEE Xplore Digital Library
Author	Georgieva, K.S. Engelbrecht, A.P.
Copyright Year	2014
Abstract	Temporal data is common in real-world datasets. Clustering of such data allows for relationships between data patterns over time to be discovered. Differential evolution (DE) algorithms have previously been used to cluster temporal data. This paper proposes the cooperative data clustering dynamic DE algorithm (CDCDynDE), which is an adaptation to the data clustering dynamic DE (DCDynDE) algorithm where each population searches for a single cluster centroid. The paper applies the proposed algorithm to a variety of temporal datasets with different frequencies of change, severities of change, dataset dimensions and data migration types. The clustering results of the cooperative data clustering DynDE are compared against the original data clustering DynDE, the re-initialising data clustering DE and the standard data clustering DE. A statistical analysis of these results shows that the cooperative data clustering DynDE algorithm obtains better data clustering solutions to the other three algorithms despite changes in frequency, severity, dimension and data migration types.
Starting Page	437
Ending Page	444
File Size	463683
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900344
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Sociology Statistics Heuristic algorithms Indexes Vectors Standards
Content Type	Text
Resource Type	Article

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