An Analysis on Density Based Clustering of Multi Dimensional Spatial Data

Mining knowledge from large amounts of spatial data is known as spatial data mining. It becomes a highly demanding field because hugeamounts of spatial data have been collected in various applications ranging from geo-spatial data to bio-medical knowledge. The amount ofspatial data being collected is increasing exponentially. So, it far exceeded human’s ability to analyze. Recently, clustering has been recognized asa primary data mining method for knowledge discovery in spatial database. The development of clustering algorithms has received a lot ofattention in the last few years and new clustering algorithms are proposed. DBSCAN is a pioneer density based clustering algorithm. It can findout the clusters of different shapes and sizes from the large amount of data containing noise and outliers. This paper shows the results ofanalyzing the properties of density based clustering characteristics of three clustering algorithms namely DBSCAN, k-means and SOM usinsynthetic two dimensional spatial data sets