Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/54558
Título: | Mining significant change patterns in multidimensional spaces |
Autor(es): | Alves, Ronnie Cley Oliveira Ribeiro, Joel Belo, Orlando |
Palavras-chave: | Change analysis Multidimensional data mining Ranking cubes Cube gradients OLAP mining e OLAP mining |
Data: | 16-Nov-2009 |
Editora: | Inderscience |
Revista: | International Journal of Business Intelligence and Data Mining |
Resumo(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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/54558 |
DOI: | 10.1504/IJBIDM.2009.029073 |
ISSN: | 1743-8187 |
e-ISSN: | 1743-8195 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito autor |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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2009-JN-IJBIDM-AlvesEtAl.pdf Acesso restrito! | 586,76 kB | Adobe PDF | Ver/Abrir |