Multivariate microaggregation by iterative optimization
Applied Intelligence
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In this paper, we propose an efficient clustering algorithm that has been applied to the microaggregation problem. The goal is to partition $(N)$ given records into clusters, each of them grouping at least $(K)$ records, so that the sum of the within-partition squared error (SSE) is minimized. We propose a successive Group Selection algorithm that approximately solves the microaggregation problem in $(O(N^2 \log N))$ time, based on sequential Minimization of SSE. Experimental results and comparisons to existing methods with similar computation cost on real and synthetic data sets demonstrate the high performance and robustness of the proposed scheme.