Mining frequent items in OLAP

Abstract

On-line analytical (OLAP) is a data summarization and aggregation tool that helps simplify data analyzing where containing in the data warehouse. However, OLAP is some different with data mining tools, which discover the implicit patterns and interesting knowledge in large amount of databases. In this study, we propose to translate the frequent pattern tree structure into the 3-D multidimensional data structure. The frequent pattern tree is used for generating compact set of frequent patterns, so the 3-D multidimensional data structure, which is converted by FP-tree is only storage the frequent patterns. And then import the multidimensional data structure into the OLAP tool to discover the interesting knowledge. The efficiency is in three aspects: (1) because the frequent pattern tree is mining the complete set of frequent patterns that helps only analyzing the meaningful patterns in data warehouse. (2) It integrates OLAP with data mining and mining knowledge in multidimensional databases. © 2011 Springer-Verlag.