Improved methods for extracting frequent itemsets from interim-support trees

Coenen, F; Leng, P; Pagourtzis, A; Rytter, W; Souliou, D

dc.contributor.author	Coenen, F	en
dc.contributor.author	Leng, P	en
dc.contributor.author	Pagourtzis, A	en
dc.contributor.author	Rytter, W	en
dc.contributor.author	Souliou, D	en
dc.date.accessioned	2014-03-01T01:30:52Z
dc.date.available	2014-03-01T01:30:52Z
dc.date.issued	2009	en
dc.identifier.issn	0038-0644	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19666
dc.subject	Association rules	en
dc.subject	Data mining	en
dc.subject	Frequent itemsets	en
dc.subject	Set-enumeration trees	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.other	Computational tasks	en
dc.subject.other	Data mining and knowledge discoveries	en
dc.subject.other	Experimental comparisons	en
dc.subject.other	Frequent itemsets	en
dc.subject.other	Frequent sets	en
dc.subject.other	Heidelberg	en
dc.subject.other	Improved methods	en
dc.subject.other	Item sets	en
dc.subject.other	Lecture Notes	en
dc.subject.other	Mining association rules	en
dc.subject.other	Relational database	en
dc.subject.other	Set-enumeration trees	en
dc.subject.other	Tree pruning	en
dc.subject.other	Tree structures	en
dc.subject.other	Algorithms	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Association rules	en
dc.subject.other	Associative processing	en
dc.subject.other	Knowledge based systems	en
dc.subject.other	Knowledge management	en
dc.subject.other	Trees (mathematics)	en
dc.title	Improved methods for extracting frequent itemsets from interim-support trees	en
heal.type	journalArticle	en
heal.identifier.primary	10.1002/spe.902	en
heal.identifier.secondary	http://dx.doi.org/10.1002/spe.902	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Mining association rules in relational databases is a significant computational task with lots of applications. A fundamental ingredient of this task is the discovery of sets of attributes (itemsets) whose frequency in the data exceeds some threshold value. In this paper we describe two algorithms for completing the calculation of frequent sets using a tree structure for storing partial supports, called interim-support (IS) tree. The first of our algorithms (T-Tree-First (TTF)) uses a novel tree pruning technique, based on the notion of (fixed-prefix) potential inclusion, which is specially designed for trees that are implemented using only two pointers per node. This allows to implement the IS tree in a space-efficient manner. The second algorithm (P-Tree-First (PTF)) explores the idea of storing the frequent itemsets in a second tree structure, called the total support tree (T-tree); the main innovation lies in the use of multiple pointers per node, which provides rapid access to the nodes of the T-tree and makes it possible to design a new, usually faster, method for updating them. Experimental comparison shows that these techniques result in considerable speedup for both algorithms compared with earlier approaches that also use IS trees (Principles of Data Mining and Knowledge Discovery, Proceedings of the 5th European Conference, PKDD, 2001, Freiburg, September 2001 (Lecture Notes in Artificial Intelligence, vol. 2168). Springer: Berlin, Heidelberg, 54-66, Journal of Knowledge-Based Syst. 2000, 13:141-149). Further comparison between the two new algorithms, shows that the PTF is generally faster on instances with a large number of frequent itemsets, provided that they are relatively short, whereas TTF is more appropriate whenever there exist few or quite long frequent itemsets, in addition. TTF behaves well on instances in which the densities of the items of file database have a high variance. Copyright (C) 2008 John Wiley & Sons. Ltd.	en
heal.publisher	JOHN WILEY & SONS LTD	en
heal.journalName	Software - Practice and Experience	en
dc.identifier.doi	10.1002/spe.902	en
dc.identifier.isi	ISI:000264940900001	en
dc.identifier.volume	39	en
dc.identifier.issue	6	en
dc.identifier.spage	551	en
dc.identifier.epage	571	en