Multi-Cluster Based Temporal Mobile Sequential Pattern Mining Using Heuristic Search

An enhanced mobile sequential pattern mining using heuristic search technique is explored to predict mobile user’s behavior effectively. By analyzing the movement of mobile users with respect to time, location and service request, one can contend that users in different user groups may have different mobile transaction behavior. Similar transaction behavior in a set is grouped by applying heuristic search technique. The heuristic search technique efficiently performs search on mobile transaction database to prune undesired transaction to form cluster and make them refined, meaningful, and relevant to the query. Research on multicluster based sequential pattern mining has been emerging in recent years due to a wide range of potential applications. One of the active topics is the facilitation of wireless and web technologies to the mobile user’s through the usage of mobile devices at anytime and anywhere. This approach has been evaluated with the transactional dataset and simulation is carried out with data obtained from the real world to generate the required network environment. Compared with Cluster-based Temporal Mobile Sequential Pattern (CTMSP), the evaluation results show that Multi-Cluster based Temporal Mobile Sequential Pattern using Heuristic Search (MCTMSPHS) achieves 30 to 40% more in accuracy, 40 to 50% less in energy usage and 20 to 25% less in execution time. Key-Words: Mobile Sequential Pattern Mining, Heuristic Search, Clustering, Mobile Environment