Implementing Cognitive Apps Based Key Generation System

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 both runtime and space usage when the support threshold is low and the patterns become long. In this paper, we present BIDE, an efficient algorithm for mining frequent closed sequences without candidate maintenance. It adopts a novel sequence closure checking scheme called BI-Directional Extension and prunes the search space more deeply compared to the previous algorithms by using the Back Scan pruning method. A thorough performance study with both sparse and dense, real, and synthetic data sets have demonstrated that BIDE significantly outperforms the previous algorithm. It reveals only a small subset of the concept nodes finally the expected user navigation cost is minimized.