Pattern Discovery:Inference Analysis Approach

Previous studies have presented convincing arguments that a frequent pattern mining algorithm should not mine all frequent patterns but only the closed ones because the latter leads to not only a more compact yet complete result set but also better efficiency. However, most of the previously developed closed pattern mining algorithms work under the candidate maintenance-and test paradigm, which is inherently costly in terms of runtime and space usage when the support threshold is low or the patterns, become long. In this paper, we propose, a new pattern mining algorithm by appending back scan pruning technique to the coherent rule algorithm which will discover domain knowledge report using coherent rules, where coherent rules would be discovered based on the properties of propositional logic, so it does not require background knowledge to generate rules. Coherent rule algorithm generates both frequent and infrequent rules. The aim of this paper is to generate only frequent rules. This new approach of back scan pruning technique prunes all the infrequent rules which results in fast retrieval of the rules, saves memory space and process time. This paper proposes the use of both upward and downward closure property for the extraction of frequent item sets which reduces the total number of scans required for the generation of coherent rules.