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An efficient enhanced k-means clustering algorithm
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 theExpand
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An Enhanced Density Based Spatial clustering of Applications with Noise
TLDR
This paper introduces an enhanced version of the DBSCAN algorithm which is able to discover clusters with varying densities. Expand
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Scalable Varied Density Clustering Algorithm for Large Datasets
TLDR
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
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A Clustering Algorithm for Discovering Varied Density Clusters
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
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K-Means for Spherical Clusters with Large Variance in Sizes
TLDR
We introduce a new procedure to refine the end of the k-means clustering algorithm that improves the quality of the resulting clusters. Expand
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Density Clustering Based On Radius of Data (DCBRD)
TLDR
We propose a density based clustering algorithm that discovers arbitrary shaped clusters, requires no input parameters and uses the same definitions of DBSCAN. Expand
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DCBOR: A Density Clustering Based on Outlier Removal
TLDR
We present a clustering algorithm, that introduces a middle ground between the hierarchical clustering and density clustering, this algorithm called DCBOR. Expand
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Density Clustering Algorithm Based on Radius of Data (DCBRD)
TLDR
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
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A Clustering Algorithm for Varied Density Clusters based on Similarity of Local Density of Objects
  • A. Fahim
  • Computer Science
  • 4th International Conference on Intelligent…
  • 1 May 2020
TLDR
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