1. Mining significant change patterns in multidimensional spaces
- Author
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Joel Ribeiro, Ronnie Alves, Orlando Belo, Information Systems IE&IS, and Universidade do Minho
- Subjects
Data processing ,Information Systems and Management ,OLAP mining ,Computer science ,Online analytical processing ,Rank (computer programming) ,e OLAP mining ,Process (computing) ,02 engineering and technology ,computer.software_genre ,Change analysis ,Management Information Systems ,Information extraction ,Cube gradients ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Effective method ,020201 artificial intelligence & image processing ,Data mining ,Statistics, Probability and Uncertainty ,Heuristics ,computer ,Multidimensional data mining ,Ranking cubes ,Event (probability theory) - Abstract
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., (undefined), info:eu-repo/semantics/publishedVersion
- Published
- 2009