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Symmetry 2018, 10(7), 262;

Iterative Group Decomposition for Refining Microaggregation Solutions

Faculty of Management Science, Nakhon Ratchasima Rajabhat University, Nakhon Ratchasima 30000, Thailand
Department of Information Management, Yuan Ze University, Taoyuan 32003, Taiwan
Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 32003, Taiwan
Author to whom correspondence should be addressed.
Received: 28 May 2018 / Revised: 17 June 2018 / Accepted: 2 July 2018 / Published: 4 July 2018
(This article belongs to the Special Issue Information Technology and Its Applications 2018)
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Microaggregation refers to partitioning n given records into groups of at least k records each to minimize the sum of the within-group squared error. Because microaggregation is non-deterministic polynomial-time hard for multivariate data, most existing approaches are heuristic based and derive a solution within a reasonable timeframe. We propose an algorithm for refining the solutions generated using the existing microaggregation approaches. The proposed algorithm refines a solution by iteratively either decomposing or shrinking the groups in the solution. Experimental results demonstrated that the proposed algorithm effectively reduces the information loss of a solution. View Full-Text
Keywords: clustering; partitioning; microaggregation clustering; partitioning; microaggregation

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Khomnotai, L.; Lin, J.-L.; Peng, Z.-Q.; Santra, A.S. Iterative Group Decomposition for Refining Microaggregation Solutions. Symmetry 2018, 10, 262.

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