Clustering Algorithm for 2D Multi-Density Large Dataset Using Adaptive Grids

Clustering is a key data mining problem. Densitybased clustering algorithms have recently gained popularity in the data mining field. Density and grid based technique is a popular way to mine clusters in a large spatial datasets wherein clusters are regarded as dense regions than their surroundings. The attribute values and ranges of these attributes characterize the clusters In this paper we adapt a density-based clustering algorithm, Grid Density clustering using triangle subdivision (GDCT) capable of identifying arbitrary shaped embedded clusters as well as multi density clusters over massive spatial datasets. Experimental results on a wide variety of synthetic and real data sets demonstrate the effectiveness of Adaptive grids and triangle subdivision method. Keywords— Clustering, Density Based, Intrinsic Cluster, Adaptive Grids