TY - JOUR
T1 - Mining significant change patterns in multidimensional spaces
AU - Alves, R.
AU - Ribeiro, J.T.S.
AU - Belo, O.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
U2 - 10.1504/IJBIDM.2009.029073
DO - 10.1504/IJBIDM.2009.029073
M3 - Article
SN - 1743-8187
VL - 4
SP - 219
EP - 241
JO - International Journal of Business Intelligence and Data Mining
JF - International Journal of Business Intelligence and Data Mining
IS - 3/4
ER -