Title: Mining significant change patterns in multidimensional spaces

Authors: Ronnie Alves, Joel Ribeiro, Orlando Belo

Addresses: Institute of Signalling, Developmental Biology and Cancer Research, Laboratory of Virtual Biology, CNRS UMR 6543 Parc Valnore, Nice 06108, Cedex 02, France. ' Department of Informatics, School of Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal. ' Department of Informatics, School of Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal

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.

Keywords: change analysis; multidimensional data mining; ranking cubes; cube gradients; OLAP mining; online analytical processing; change patterns; multidimensional spaces; gradient search; partitioning; data cubing; periodicity awareness.

DOI: 10.1504/IJBIDM.2009.029073

International Journal of Business Intelligence and Data Mining, 2009 Vol.4 No.3/4, pp.219 - 241

Published online: 03 Nov 2009 *

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