A Hybrid Framework using Fuzzy if-then rules for DBSCAN Algorithm

DBSCAN is a density-based clustering algorithm. This algorithm clusters data of high density. For finding core objects traditional DBSCAN uses this core object as center core which extends outwards continuously. As core objects are growing, the unprocessed objects which are retained in memory, will occupy a lot of memory and I/O overhead which tends to low efficiency of algorithm. A data mining technique which is applied for large databases, a DBSCAN works on the basis of bivalent logic. Hence it can only identify objects which completely belonging to a particular cluster or not wholly belonging to it. In this paper, a framework of methodology of DBSCAN algorithm with the integration of fuzzy logic is presented. The improved version is hybridization of DBSCAN algorithm with fuzzy if-then rules.