A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases

Abstract

DBSCAN is a base algorithm for density based clustering. It can detect the clusters of different shapes and sizes from the large amount of data which contains noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. In this paper, we propose a density varied DBSCAN algorithm which is capable to handle local density variation within the cluster. It calculates the growing cluster density mean and then the cluster density variance for any core object, which is supposed to be expended further, by considering density of its -neighborhood with respect to cluster density mean. If cluster density variance for a core object is less than or equal to a threshold value and also satisfying the cluster similarity index, then it will allow the core object for expansion. The experimental results show that the proposed clustering algorithm gives optimized results. General TermsDensity Based Clustering

A Comparative Study of Different Density based Spatial Clustering Algorithms

“…Ram et al [19] presents DVBSCAN, an extension of DBSCAN algorithm to handle the local density variation that exists within the cluster based on the concept that it starts the creation of the cluster by selecting core object. Then, it computes Cluster Density Mean (CDM) of the growing cluster before allowing the expansion of an unprocessed core object.…”

A Density based clustering with Artificial Immunity inspired preprocessing

“…EXPERIMENTAL RESULTS (14) To prove the efficiency of AIDCOR, we performed experimental evaluation of our algorithm with wide variety of data set and compared with one of the most efficient clustering algorithm -DBSCAN. Both Where gmn is 0 if Agm and Agn either belong to the same cluster or in different clusters as they are in refciust.…”

“…There are also several density based clustering algorithms proposed in literature [2], [13], [14], [8]. We adopted some concepts of DBSCAN [2] like core points, border points and density connected points and also introduced some new concepts like Local Reachability Factor, Global Reachability Factor, Reachability Multiplier (Section IV).…”

PFU: Profiling Forum users in online social networks, a knowledge driven data mining approach

“…Experiments conducted on synthetic sample database and SE-QUOIA 2000 database shows that the run time of DBSCAN is slightly higher than linear in the number of points and is more effective than Clustering Large Applications based on RANdomized Search (CLARANS) algorithm in discovering clusters of arbitrary shape. Based on two important directions:-clustering point objects, spatial and their non-spatial attributes, Generalized Density-Based Spatial Clustering of Applications with Noise (GDBSCAN) algorithm is proposed by Ram et al [11]. Results obtained after experimenting shows effectiveness and efficiency of GDBSCAN on large spatial databases.…”