A minimum spanning tree equipartition algorithm for microaggregation

References

• N. Anwar, Micro-aggregation-the small aggregates method, Tech. Rep. Internal report. Luxembourg: Eurostat, 1993.
   Google Scholar
• C.-C. Chang, Y.-C. Li, and W.-H. Huang, TFRP: An efficient microaggregation algorithm for statistical disclosure control, J. Syst. Softw. 80 (2007), pp. 1866–1878. doi: 10.1016/j.jss.2007.02.014
   Web of Science ®Google Scholar
• D. Defays and P. Nanopoulos, Panels of enterprises and confidentiality: The small aggregates method, Proceedings of the 1992 Symposium on Design and Analysis of Longitudinal Surveys, 1993, pp. 195–204.
   Google Scholar
• J. Domingo-Ferrer and J.M. Mateo-Sanz, Practical data-oriented microaggregation for statistical disclosure control, IEEE Trans. Knowl. Data Eng. 14 (2002), pp. 189–201. doi: 10.1109/69.979982
   Web of Science ®Google Scholar
• J. Domingo-Ferrer, A. Martinez-Balleste, J.M. Mateo-Sanz, and F. Sebe, Efficient multivariate data-oriented microaggregation, VLDB J. 15 (2006), pp. 355–369. doi: 10.1007/s00778-006-0007-0
   Web of Science ®Google Scholar
• J. Domingo-Ferrer, F. Sebé, and A. Solanas, A polynomial-time approximation to optimal multivariate microaggregation, Comput. Math. Appl. 55 (2008), pp. 714–732. doi: 10.1016/j.camwa.2007.04.034
   Web of Science ®Google Scholar
• S.L. Hansen and S. Mukherjee, A polynomial algorithm for optimal univariate microaggregation, IEEE Trans. Knowl. Data Eng. 15 (2003), pp. 1043–1044. doi: 10.1109/TKDE.2003.1209020
   Web of Science ®Google Scholar
• T. Kanungo, D.M. Mount, N.S. Netanyahu, C.D. Piatko, R. Silverman, and A.Y. Wu, An efficient k-means clustering algorithm: Analysis and implementation, IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002), pp. 881–892. doi: 10.1109/TPAMI.2002.1017616
   Web of Science ®Google Scholar
• M. Laszlo and S. Mukherjee, Minimum spanning tree partitioning algorithm for microaggregation, IEEE Trans. Knowl. Data Eng. 17 (2005), pp. 902–911. doi: 10.1109/TKDE.2005.112
   Web of Science ®Google Scholar
• J. Nin, J. Herranz, and V. Torra, On the disclosure risk of multivariate microaggregation, Data Knowl. Eng. 67 (2008), pp. 399–412. doi: 10.1016/j.datak.2008.06.014
   Web of Science ®Google Scholar
• A. Oganian and J. Domingo-Ferrer, On the complexity of optimal microaggregation for statistical disclosure control, Statist. J. 18 (2001), pp. 345–354.
   Google Scholar
• C. Panagiotakis, K. Athanassopoulos, and G. Tziritas, The equipartition of curves, Comput. Geom. 42 (2009), pp. 677–689. doi: 10.1016/j.comgeo.2009.01.003
   Web of Science ®Google Scholar
• C. Panagiotakis, I. Grinias, and G. Tziritas, Natural image segmentation based on tree equipartition, Bayesian flooding and region merging, IEEE Trans. Image Process. 20 (2011), pp. 2276–2287. doi: 10.1109/TIP.2011.2114893
   PubMed Web of Science ®Google Scholar
• C. Panagiotakis and G. Tziritas, Successive group selection for microaggregation, IEEE Trans. Knowl. Data Eng. 25 (2013), pp. 1191–1195. doi: 10.1109/TKDE.2011.242
   Web of Science ®Google Scholar
• R. Prim, Shortest connection networks and some generalizations, Bell Syst. Tech. J. 36 (1957), pp. 1389–1401. doi: 10.1002/j.1538-7305.1957.tb01515.x
   Google Scholar
• A. Quatember and M.C. Hausner, A family of methods for statistical disclosure control, J. Appl. Stat. 40 (2013), pp. 337–346. doi: 10.1080/02664763.2012.743975
   Web of Science ®Google Scholar
• P.L. Rosin, Techniques for assessing polygonal approximations of curves, IEEE Trans. Pattern Anal. Mach. Intell. 19 (1997), pp. 659–666. doi: 10.1109/34.601253
   Web of Science ®Google Scholar
• M. Solé, V. Muntés-Mulero, and J. Nin, Efficient microaggregation techniques for large numerical data volumes, Int. J. Inf. Secur. 11 (2012), pp. 253–267. doi: 10.1007/s10207-012-0158-5
   Web of Science ®Google Scholar
• L.C. Willenborg and T.D. Waal, Elements of Statistical Disclosure Control, Vol. 155, Springer Verlag, New York, 2001.
   Google Scholar
• B.Y. Wu, H.L. Wang, S. Ta Kuan, and K.M. Chao, On the uniform edge-partition of a tree, Discrete Appl. Math. 155 (2007), pp. 1213–1223. doi: 10.1016/j.dam.2006.10.012
   Web of Science ®Google Scholar
• C.T. Zahn, Graph-theoretical methods for detecting and describing gestalt clusters, IEEE Trans. Comput. 20 (1971), pp. 68–86. doi: 10.1109/T-C.1971.223083
   Web of Science ®Google Scholar