The issue of determining ‘the right number of clusters’ is attracting ever growing interest. The paper reviews published work on the issue with respect to mixture of distributions, partition, especially in k-means clustering, and hierarchical cluster structures. Some perspective directions for further developments are outlined
3We propose a tool for exploring the number of clusters based on pivotal methods and consensus clust...
Data clustering is a data exploration technique that allows objects with similar characteristics to ...
Cluster Analytics helps to analyze the massive amounts of data which have accrued in this technologi...
The issue of determining ‘the right number of clusters’ is attracting ever growing interest. The pap...
Clustering analysis seeks to partition a given dataset into groups or clusters so that the data obje...
Abstract: A fundamental and difficult problem in cluster analysis is the determination of the “true...
Today's data mostly does not include the knowledge of cluster number. Therefore, it is not possible ...
In cluster analysis, identifying the number of clusters in a dataset is one of the most important pr...
The issue of determining “the right number of clusters” in K-Means has attracted considerable intere...
A large number of classification and clustering methods for defining and calculating optimal or well...
Abstract: The K-means algorithm is a popular data-clustering algorithm. However, one of its drawback...
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Clustering procedures partiti...
101 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.Clustering and classification...
101 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.Clustering and classification...
Partitioning data into a finite number of k homogenous and separate clusters (groups) without use of...
3We propose a tool for exploring the number of clusters based on pivotal methods and consensus clust...
Data clustering is a data exploration technique that allows objects with similar characteristics to ...
Cluster Analytics helps to analyze the massive amounts of data which have accrued in this technologi...
The issue of determining ‘the right number of clusters’ is attracting ever growing interest. The pap...
Clustering analysis seeks to partition a given dataset into groups or clusters so that the data obje...
Abstract: A fundamental and difficult problem in cluster analysis is the determination of the “true...
Today's data mostly does not include the knowledge of cluster number. Therefore, it is not possible ...
In cluster analysis, identifying the number of clusters in a dataset is one of the most important pr...
The issue of determining “the right number of clusters” in K-Means has attracted considerable intere...
A large number of classification and clustering methods for defining and calculating optimal or well...
Abstract: The K-means algorithm is a popular data-clustering algorithm. However, one of its drawback...
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Clustering procedures partiti...
101 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.Clustering and classification...
101 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.Clustering and classification...
Partitioning data into a finite number of k homogenous and separate clusters (groups) without use of...
3We propose a tool for exploring the number of clusters based on pivotal methods and consensus clust...
Data clustering is a data exploration technique that allows objects with similar characteristics to ...
Cluster Analytics helps to analyze the massive amounts of data which have accrued in this technologi...
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