Bregman divergences in the (m × k)-partitioning problem

Kokolakis, G; Nanopoulos, Ph; Fouskakis, D

dc.contributor.author	Kokolakis, G	en
dc.contributor.author	Nanopoulos, Ph	en
dc.contributor.author	Fouskakis, D	en
dc.date.accessioned	2014-03-01T01:23:41Z
dc.date.available	2014-03-01T01:23:41Z
dc.date.issued	2006	en
dc.identifier.issn	0167-9473	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17084
dc.subject	Bregman divergences	en
dc.subject	Confidentiality	en
dc.subject	Convex partition	en
dc.subject	Data masking	en
dc.subject	Fixed cardinality partitioning	en
dc.subject	Fixed size micro-aggregation	en
dc.subject	Pythagorean property	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	Data privacy	en
dc.subject.other	Data reduction	en
dc.subject.other	Probability	en
dc.subject.other	Real time systems	en
dc.subject.other	Security of data	en
dc.subject.other	Bregman divergences	en
dc.subject.other	Confidentiality	en
dc.subject.other	Convex partition	en
dc.subject.other	Data masking	en
dc.subject.other	Fixed cardinality partitioning	en
dc.subject.other	Fixed size micro-aggregation	en
dc.subject.other	Pythagorean property	en
dc.subject.other	Problem solving	en
dc.title	Bregman divergences in the (m × k)-partitioning problem	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.csda.2006.02.017	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.csda.2006.02.017	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	A method of fixed cardinality partition is examined. This methodology can be applied on many problems, such as the confidentiality protection, in which the protection of confidential information has to be ensured, while preserving the information content of the data. The basic feature of the technique is to aggregate the data into m groups of small fixed size k, by minimizing Bregman divergences. It is shown that, in the case of non-uniform probability measures the groups of the optimal solution are not necessarily separated by hyperplanes, while with uniform they are. After the creation of an initial partition on a real data-set, an algorithm, based on two different Bregman divergences, is proposed and applied. This methodology provides us with a very fast and efficient tool to construct a near-optimum partition for the (m x k)-partitioning problem. (c) 2006 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Computational Statistics and Data Analysis	en
dc.identifier.doi	10.1016/j.csda.2006.02.017	en
dc.identifier.isi	ISI:000242484200018	en
dc.identifier.volume	51	en
dc.identifier.issue	2	en
dc.identifier.spage	668	en
dc.identifier.epage	678	en