New Algorithms for Best Results In Varied Densities
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An enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes, sizes that differ in local density. We introduce three new algorithms. Our first proposed algorithm Vibration Method DBSCAN (VMDBSCAN) first finds out the ¿core¿ of each cluster ¿ clusters generated after applying DBSCAN -. Then it ¿vibrates" points toward cluster that has the maximum influence on these points. The second proposed algorithm is Dynamic Method DBSCAN (DMDBSCAN). It selects several values of the radius of a number of objects (Eps) for different densities according to a k-dist plot....
An enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes, sizes that differ in local density. We introduce three new algorithms. Our first proposed algorithm Vibration Method DBSCAN (VMDBSCAN) first finds out the ¿core¿ of each cluster ¿ clusters generated after applying DBSCAN -. Then it ¿vibrates" points toward cluster that has the maximum influence on these points. The second proposed algorithm is Dynamic Method DBSCAN (DMDBSCAN). It selects several values of the radius of a number of objects (Eps) for different densities according to a k-dist plot. For each value of Eps, DBSCAN algorithm is adopted in order to make sure that all the clusters with respect to corresponding density are clustered. Next the points that have been clustered are ignored, which avoids marking both denser areas and sparser ones as one cluster. The last algorithm Vibration and Dynamic DBSCAN (VDDBSCAN) combines the first and the second algorithms. It begins by searching for each level of density to its corresponding Eps, then it will use DBSCAN to find all clusters, finally, it will use vibration method of VMDBSCAN to solve the problem of splitting clusters
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