Estimating Prevalence Bounds of Temporal Association Patterns to Discover Temporally Similar Patterns

摘要

Mining Temporal Patterns from temporal databases is challenging as it requires handling efficient database scan. A pattern is temporally similar when it satisfies subset constraints. The naive and apriori algorithm designed for non-temporal databases cannot be extended to find similar temporal patterns from temporal databases. The brute force approach requires computing 2n true support combinations for 'n' items from finite item set and falls in NP-class. The apriori or fp-tree based approaches are not directly extendable to temporal databases to obtain similar temporal patterns. In this present research, we come up with novel approach to discover temporal association patterns which are similar for pre-specified subset constraints, and substantially reduce support computations by defining expressions to estimate support bounds. The proposed approach eliminates computational overhead in finding similar temporal patterns. The results prove that the proposed method outperforms brute force approach.