Weighted association rule mining based on PCA algorithm in wireless communication network

The concept of finding the significant communication relation in the wireless network only with the behavioral data is of great importance for non-authorized monitor. A valuable technology to solve the problem is the weighted association rules mining algorithm. However, classical models ignore the difference between items, and many weighted association rule mining algorithms do not work without a pre-assigned weight or work with a high complexity. In this paper, we introduce a novel measure p-weight considering the property of the behavioral database and propose a weighted association rule mining algorithm based on the primary components analysis (PCA). Performance of the proposed algorithm is compared with HITS algorithm and the other existing algorithm. It is observed that for the behavioral database of the wireless network, there is drastic reduction in the computational time for the proposed algorithm.