Add to ORKGTechniques based on agglomerative hierarchical clustering constitute one of the most frequent approaches in unsupervised clustering. Some are based on the single linkage methodology, which has been shown to produce good results with sets of clusters of various sizes and shapes. However, the application of this type of algorithms in a wide variety of fields has posed a number of problems, such as the sensitivity to outliers and fluctuations in the density of data points. Additionally, these algorithms do not usually allow for automatic clustering.http://www.sciencedirect.com/science/article/B6TFP-4MYVG3H-1/1/714f82559ca43bbdbff8ad8a1e2d14a
Clustering algorithms have evolved to handle more and more complex structures. However, the measures...
Abstract: In modern era there are lots of data mining algorithms which focus on clustering methods. ...
This paper describes a methodology for the application of hierarchical clustering methods to the ta...
Techniques based on agglomerative hierarchical clustering constitute one of the most frequent approa...
An integrated framework for density-based cluster analysis, outlier detection, and data visualizatio...
6 pagesInternational audienceClustering methods usually require to know the best number of clusters,...
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Man...
6 pagesInternational audienceClustering methods usually require to know the best number of clusters,...
International audienceIt is well known that the classical single linkage algorithm usually fails to ...
In this paper we present a method to detect natural groups in a data set, based on hierarchical clus...
An integrated framework for density-based cluster analysis, outlier detection, and data visualizatio...
An integrated framework for density-based cluster analysis, outlier detection, and data visualizatio...
Abstract: A fundamental and difficult problem in cluster analysis is the determination of the “true...
Clustering analysis is one of the most commonly used techniques for uncovering patterns in data mini...
Determining an optimal number of clusters and producing reliable results are two challenging and cri...
Clustering algorithms have evolved to handle more and more complex structures. However, the measures...
Abstract: In modern era there are lots of data mining algorithms which focus on clustering methods. ...
This paper describes a methodology for the application of hierarchical clustering methods to the ta...
Techniques based on agglomerative hierarchical clustering constitute one of the most frequent approa...
An integrated framework for density-based cluster analysis, outlier detection, and data visualizatio...
6 pagesInternational audienceClustering methods usually require to know the best number of clusters,...
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Man...
6 pagesInternational audienceClustering methods usually require to know the best number of clusters,...
International audienceIt is well known that the classical single linkage algorithm usually fails to ...
In this paper we present a method to detect natural groups in a data set, based on hierarchical clus...
An integrated framework for density-based cluster analysis, outlier detection, and data visualizatio...
An integrated framework for density-based cluster analysis, outlier detection, and data visualizatio...
Abstract: A fundamental and difficult problem in cluster analysis is the determination of the “true...
Clustering analysis is one of the most commonly used techniques for uncovering patterns in data mini...
Determining an optimal number of clusters and producing reliable results are two challenging and cri...
Clustering algorithms have evolved to handle more and more complex structures. However, the measures...
Abstract: In modern era there are lots of data mining algorithms which focus on clustering methods. ...
This paper describes a methodology for the application of hierarchical clustering methods to the ta...