On Line Mining of Cyclic Association Rules From Parallel Dimension Hierarchies

Abstract

The decision-making process can be supported by many pioneering tech-nologies such as Data Warehouse (DW), On-Line Analytical Processing (OLAP), and Data Mining (DM). Much research found in literature is aimed at integrating these popular research topics. In this chapter, we focus on discovering cyclic patterns from advanced multi-dimensional context, specially parallel hierarchies where more than one hierarchy is associated to given dimension in respect to several analytical purposes. Thus, we introduce a new framework for cyclic association rules mining from multiple hierarchies. To exemplify our proposal, an illustrative example is pro-vided throughout the article. Finally, we perform intensive experiments on synthetic and real data to emphasize the interest of our approach.