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Hybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance Cover

Hybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance

Cybernetics and Information Technologies
Volume 16 (2016): Issue 2 (June 2016)
By:  and    
Open Access
|Jun 2016
References

References

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DOI: https://doi.org/10.1515/cait-2016-0017 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702 (formerly 1314-4081)
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Language: English
Page range: 16 - 34
Published on: Jun 22, 2016
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Keywords:
Hybridization,
clustering,
K-means,
mixture models,
expectation maximization,
clustering fitness,
sum of squared errors
Related subjects:
Computer sciences,
Information technology

© 2016 D. Raja Kishor, N. B. Venkateswarlu, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 16 (2016): Issue 2 (June 2016)
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