Clustering-Based Aggregation of High-Utility Patterns from Unknown Multi-database

High-utility patterns generated from mining the unknown and different databases can be clustered to identify the most valid patterns. Sources include the internet, journals, and enterprise data. Here, a grid-based clustering method (CLIQUE) is used to aggregate patterns mined from multiple databases. The proposed model forms the clusters based on all the utilities of patterns to determine the interestingness and the correct interval of its utility measure. The set of all patterns is collected by first mining the databases individually, at the local level. The problem arises when the same pattern is identified by all of the databases but with different utility factors. In this case, it becomes difficult to decide whether the pattern should be considered as a valid or not, due to the presence of multiple utility values. Hence, an aggregation model is applied to test whether a pattern satisfies the utility threshold set by a domain expert. We found that the proposed aggregation model effectively clusters all of the interesting patterns by discarding those patterns that do not satisfy the threshold condition. The proposed model accurately optimizes the utility interval of the valid patterns.