A Novel Data Preparation Approach for Target Based Association Rule Mining

Target based association rules extract the correlation between targets in and around strategically specified regions. These rules can be used to predict and estimate the future results and find the faulty targets, etc. To extract these rules efficiently and effectively data preparation is required. The existing mechanisms of data preparation have the problem of redundancy in the data. As a consequence, extra energy is required for the sensors which used to monitor the targets. In this paper, authors propose a novel data preparation approach for target based association rule mining from the point of coverage wireless sensor networks, which reduces the redundancy in the data and thus enhances the performance of the network.