In k-means clustering, we are given a set of n data points in d-dimensional space ℝd and an integer k and the problem is to determine a set of k points in ℝd, called centers, so as to minimize the… Expand

In this paper, we aim at enhancing the well-known algorithm DBSCAN, to make it scalable and able to discover clusters from uneven datasets in which clusters are regions of homogenous density.Expand

Spatial data clustering is one of the important data mining techniques for extracting knowledge from large amount of spatial data collected in various applications, such as remote sensing, GIS,… Expand

We propose a density based clustering algorithm that discovers arbitrary shaped clusters, requires no input parameters and uses the same definitions of DBSCAN.Expand

We present a clustering algorithm, that introduces a middle ground between the hierarchical clustering and density clustering, this algorithm called DCBOR.Expand

We propose a density based clustering algorithm based on a knowledge acquired from the data which is designed to discover clusters of arbitrary shape.Expand

We propose a clustering algorithm for datasets that contain diverse clusters in density; the cluster is a connected graph where the similarity between any two adjacent neighbors is greater than or equal to a threshold.Expand