Add to ORKGThe various-density problem has become one of the focuses in density based clustering research. A novel dispersive degree based algorithm combined with classification, called CDDC, is presented in this paper to remove the hurdle. In CDDC, a sequence is established for depicting the data distribution, discriminating cores and classifying edges. Clusters are discovered by utilizing the revealed information. Several experiments are performed and the results suggest that CDDC is effective in handling the various-density problem and is more efficient than the well-known algorithms such as DBSCAN, OPTICS and KNNCLUST
The k_means clustering algorithm has very extensive application. The paper gives out_in clustering a...
Data objects with mixed numerical and categorical attributes are often dealt with in the real world....
Clustering algorithms are attractive for the task of class identification in spatial databases. Howe...
Abstract: Distributed clustering is an effect method for solving the problem of clustering data loc...
Finding clusters in data is a challenging problem especially when the clusters are being of widely v...
One of the main categories in Data Clustering is density based clustering. Density based clustering ...
The density based algorithms considered as one of the most common and powerful algorithms in data cl...
Density-based spatial clustering of applications with noise (DBSCAN) is a base algorithm for density...
Here, the authors propose a novel two-phase clustering algorithm with a density exploring distance (...
Clustering methods in data mining are widely used to detect hotspots in many domains. They play an i...
Paper presented at the International Conference on Computational Intelligence for Modelling, Control...
The performance of density based clustering algorithms may be greatly influenced by the chosen param...
Paper presented at the 2008 International Conference on Computer Science and Software Engineering, D...
Due to the adoption of global parameters, DBSCAN fails to identify clusters with different and varie...
Density-based clustering is a sort of clustering analysis methods, which can discover clusters with ...
The k_means clustering algorithm has very extensive application. The paper gives out_in clustering a...
Data objects with mixed numerical and categorical attributes are often dealt with in the real world....
Clustering algorithms are attractive for the task of class identification in spatial databases. Howe...
Abstract: Distributed clustering is an effect method for solving the problem of clustering data loc...
Finding clusters in data is a challenging problem especially when the clusters are being of widely v...
One of the main categories in Data Clustering is density based clustering. Density based clustering ...
The density based algorithms considered as one of the most common and powerful algorithms in data cl...
Density-based spatial clustering of applications with noise (DBSCAN) is a base algorithm for density...
Here, the authors propose a novel two-phase clustering algorithm with a density exploring distance (...
Clustering methods in data mining are widely used to detect hotspots in many domains. They play an i...
Paper presented at the International Conference on Computational Intelligence for Modelling, Control...
The performance of density based clustering algorithms may be greatly influenced by the chosen param...
Paper presented at the 2008 International Conference on Computer Science and Software Engineering, D...
Due to the adoption of global parameters, DBSCAN fails to identify clusters with different and varie...
Density-based clustering is a sort of clustering analysis methods, which can discover clusters with ...
The k_means clustering algorithm has very extensive application. The paper gives out_in clustering a...
Data objects with mixed numerical and categorical attributes are often dealt with in the real world....
Clustering algorithms are attractive for the task of class identification in spatial databases. Howe...