Mining chains of relations

Afrati, F; Das, G; Gionis, A; Mannila, H; Mielikainen, T; Tsaparas, P

dc.contributor.author	Afrati, F	en
dc.contributor.author	Das, G	en
dc.contributor.author	Gionis, A	en
dc.contributor.author	Mannila, H	en
dc.contributor.author	Mielikainen, T	en
dc.contributor.author	Tsaparas, P	en
dc.date.accessioned	2014-03-01T02:43:25Z
dc.date.available	2014-03-01T02:43:25Z
dc.date.issued	2005	en
dc.identifier.issn	15504786	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31395
dc.subject	Association Rule	en
dc.subject	Data Mining	en
dc.subject	Data Mining Application	en
dc.subject	Satisfiability	en
dc.subject.other	Algorithms	en
dc.subject.other	Association rules	en
dc.subject.other	Bibliographic retrieval systems	en
dc.subject.other	Database systems	en
dc.subject.other	Problem solving	en
dc.subject.other	Mining chains	en
dc.subject.other	Relational chains	en
dc.subject.other	Data mining	en
dc.title	Mining chains of relations	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICDM.2005.94	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICDM.2005.94	en
heal.identifier.secondary	1565724	en
heal.publicationDate	2005	en
heal.abstract	Traditional data mining applications consider the problem of mining a single relation between two attributes. For example, in a scientific bibliography database, authors are related to papers, and we may be interested in discovering association rules between authors. However, in real life, we often have multiple attributes related though chains of relations. For example, authors write papers, and papers concern one or more topics. Mining such relational chains poses additional challenges. In this paper we consider the following problem: given a chain of two relations R1 (A, P) and R2(P, T) we want to find selectors for the objects in T such that the projected relation between A and P satisfies a specific property. The motivation for our approach is that a given property might not hold on the whole dataset, but it might hold when projecting the data on a selector set. We discuss various algorithms and we examine the conditions under which the apriori technique can be used. We experimentally demonstrate the effectiveness of our methods. © 2005 IEEE.	en
heal.journalName	Proceedings - IEEE International Conference on Data Mining, ICDM	en
dc.identifier.doi	10.1109/ICDM.2005.94	en
dc.identifier.spage	553	en
dc.identifier.epage	556	en