A Novel Reconfigurable Character based Token Set Pruner (RCBTSP) for Heterogeneous Environment

Extracting useful insights from large and detailed collections of data is called data mining. With the increased possibilities in modern society for companies and institutions to gather data cheaply and efficiently, this subject has become of increasing importance. This interest has inspired a rapidly maturing research field with developments both on a theoretical, as well as on a practical level with the availability of a range of commercial tools. In this paper we proposed a novel Reconfigurable Character based Token Set Pruner (RCBTSP) for heterogeneous environment. Our algorithm contains six phases 1) Authentication 2)Reading Database 3) Define the reconfigurable character 4) Define the minimum support 5) Find the token based on the minimum support 6) Prune phase. Finally our algorithm shows better performance showing the simulation result. Finally, through the simulation our proposed methods were shown to deliver excellent performance in terms of efficiency, accuracy and applicability under various system conditions.