Clustering local frequency items in multiple databases

Frequent items could be considered as a basic type of patterns in a database. In the context of multiple data sources, most of the global patterns are based on local frequency items. A multi-branch company transacting from different branches often needs to extract global patterns from data distributed over the branches. Global decisions could be taken effectively using such patterns. Thus, it is important to cluster local frequency items in multiple databases. An overview of the existing measures of association is presented here. For the purpose of selecting the suitable technique of mining multiple databases, we have surveyed the existing multi-database mining techniques. A study on the related clustering techniques is also covered here. The notion of high frequency itemsets is introduced here, and an algorithm for synthesizing supports of such itemsets is designed. The existing clustering technique might cluster local frequency items at a low level, since it estimates association among items in an itemset with a low accuracy, and thus a new algorithm for clustering local frequency items is proposed. Due to the suitability of measure of association A2, association among items in a high frequency itemset is synthesized based on it. The soundness of the clustering technique has been shown. Numerous experiments are conducted using five datasets, and the results on different aspects of the proposed problem are presented in the experimental section. The effectiveness of the proposed clustering technique is more visible in dense databases.