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Algorithms for Association Rules

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dc.contributor.authorHegland, Markus
dc.date.accessioned2015-12-13T22:37:30Z
dc.date.available2015-12-13T22:37:30Z
dc.date.issued2003
dc.date.updated2015-12-11T09:37:00Z
dc.description.abstractAssociation rules are "if-then rules" with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis, association rules are now one of the most popular tools in data mining. This popularity is to a large part due to the availability of efficient algorithms following from the development of the Apriori algorithm. We will review the basic Apriori algorithm and discuss variants for distributed data, inclusion of constraints and data taxonomies. The review ends with an outlook on tools which have the potential to deal with long itemsets and considerably reduce the amount of (uninteresting) itemsets returned. The discussion will focus on the problem of finding frequent itemsets.
dc.identifier.isbn3540005293
dc.identifier.urihttp://hdl.handle.net/1885/77129
dc.publisherSpringer
dc.relation.ispartofAdvanced Lectures on Machine Learning
dc.relation.isversionof1 Edition
dc.titleAlgorithms for Association Rules
dc.typeBook chapter
local.bibliographicCitation.lastpage234
local.bibliographicCitation.placeofpublicationGermany
local.bibliographicCitation.startpage226
local.contributor.affiliationHegland, Markus, College of Physical and Mathematical Sciences, ANU
local.contributor.authoruidHegland, Markus, u9200256
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080199 - Artificial Intelligence and Image Processing not elsewhere classified
local.identifier.ariespublicationMigratedxPub6014
local.identifier.scopusID2-s2.0-33744776550
local.type.statusPublished Version

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