OLAM Cube Selection in OMARS

• Main Authors: Min-Feng Wang, 王敏峰
• Other Authors: Wen-Yang Lin
• Format: Others
• Language: en_US
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/71551194034915260433

碩士 === 義守大學 === 資訊管理學系碩士班 === 91 === Mining association rules from large database is a data and computation intensive task. To reduce the complexity of association mining, Lin et al. proposed the concept of OLAM (On-Line Association Mining) cube, an extension of Ice-berg cube used to store frequent multidimensional itemsets. They also proposed a framework of on-line multidimensional association rule mining system, called OMARS, to provide users an environment to execute OLAP-like query to mine association rules from data warehouses efficiently. This thesis is a companion toward the implementation of OMARS. Particularly, the problem of selecting appropriate OLAM cubes to materialize and store in OMARS is concerned. And, according to the proposed mining algorithms in OMARS, we deploy the model for evaluating the cost of answering association queries using materialized OLAM cubes, which is a preliminary step for OLAM cubes selection. Besides, we modify and implement some state-of-the-art heuristic algorithms, and draw comparisons between these algorithms to evaluate their effectiveness.