Next Article in Journal
A Survey of Methods for Symmetry Detection on 3D High Point Density Models in Biomedicine
Next Article in Special Issue
The Application of a Double CUSUM Algorithm in Industrial Data Stream Anomaly Detection
Previous Article in Journal
Intuitionistic Fuzzy Multiple Attribute Decision-Making Model Based on Weighted Induced Distance Measure and Its Application to Investment Selection
Previous Article in Special Issue
RIM4J: An Architecture for Language-Supported Runtime Measurement against Malicious Bytecode in Cloud Computing
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Symmetry 2018, 10(7), 262; https://doi.org/10.3390/sym10070262

Iterative Group Decomposition for Refining Microaggregation Solutions

1
Faculty of Management Science, Nakhon Ratchasima Rajabhat University, Nakhon Ratchasima 30000, Thailand
2
Department of Information Management, Yuan Ze University, Taoyuan 32003, Taiwan
3
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)
Full-Text   |   PDF [1165 KB, uploaded 4 July 2018]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top