Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/54558

TitleMining significant change patterns in multidimensional spaces
Author(s)Alves, Ronnie Cley Oliveira
Ribeiro, Joel
Belo, Orlando
KeywordsChange analysis
Multidimensional data mining
Ranking cubes
Cube gradients
OLAP mining
e OLAP mining
Issue date16-Nov-2009
PublisherInderscience
JournalInternational Journal of Business Intelligence and Data Mining
Abstract(s)In this paper, we present a new OLAP Mining method for exploring interesting trend patterns. Our main goal is to mine the most (TOP-K) significant changes in Multidimensional Spaces (MDS) applying a gradient-based cubing strategy. The challenge is then finding maximum gradient regions, which maximises the task of detecting TOP-K gradient cells. Several heuristics are also introduced to prune MDS efficiently. In this paper, we motivate the importance of the proposed model, and present an efficient and effective method to compute it by: • evaluating significant changes by means of pushing gradient search into the partitioning process • measuring Gradient Regions (GR) spreadness for data cubing • measuring Periodicity Awareness (PA) of a change, assuring that it is a change pattern and not only an isolated event • devising a Rank Gradient-based Cubing to mine significant change patterns in MDS.
TypeArticle
URIhttp://hdl.handle.net/1822/54558
DOI10.1504/IJBIDM.2009.029073
ISSN1743-8187
e-ISSN1743-8195
Peer-Reviewedyes
AccessRestricted access (Author)
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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