| With wide applications of computers and automated data collection tools, massive amounts of data have been continuously collected and stored in databases, which creates an imminent need and great opportunities for mining interesting knowledge from data. Association rule mining is one kind of data mining techniques which discovers strong association or correlation relationships among data. The discovered rules may help market basket or cross-sales analysis, decision making, and business management.; In this thesis, we propose and develop an interesting association rule mining approach, called on-line analytical mining of association rules, which integrates the recently developed OLAP (on-line analytical processing) technology with some efficient association mining methods. It leads to flexible, multi-dimensional, multi-level association rule mining with high performance. Several algorithms are developed based on this approach for mining various kinds of associations in multi-dimensional databases, including intra-dimensional association, inter-dimensional association, hybrid association, and constraints-based association. These algorithms have been implemented in the DBMiner system. Our study shows that this approach presents great advantages over many existing algorithms in terms of both flexibility and efficiency. |
