Abstract:
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.
