Identifying Global Exceptional Patterns in Multi-database Mining

In multi-database mining, there can be many local patterns (frequent itemsets or association rules) in each database. At the end of multi-database mining, it is necessary to analyze these local patterns to gain global patterns, when putting all the data from the databases into a single dataset can destroy important information that reflect the distribution of global patterns. This paper develops an algorithm for synthesizing local patterns in multi-database is proposed. This approach is particularly fit to find potentially useful exceptions. The proposed method has been evaluated experimentally. The experimental results have shown that this method is efficient and appropriate to identifying exceptional patterns.

• Type: Journal
• Year: 2004
• Where: CIB
• Authors: Chengqi Zhang, Meiling Liu, Wenlong Nie, Shichao Zhang
• Keywords: CIB 2004, Global Patterns, Local Patterns, Multi-database Mining