Add to ORKGAn important problem in clustering research is the stability of sample clusters. Cluster diagnostics, based on the bootstrap subsampling procedure and Fowlkes and Mallows ' B statistic, are developed in this K. study to aid the users of cluster analysis in assessing the stability and validity of sample clusters. 1. INTPQDUCTIQN An important problem in clustering research is the stability and validity of the sample clusters. For Euclidea
Over the past few years, the notion of stability in data clustering has received growing attention a...
A popular method for selecting the number of clusters is based on sta-bility arguments: one chooses ...
<p>Maximum likelihood tree with the 48 transmission clusters colored in green. Maximum likelihood tr...
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1983.MICROFICHE COPY AVA...
Cluster validity investigates whether generated clusters are true clusters or due to chance. This is...
Segmentation results derived using cluster analysis depend on (1) the structure of the data and (2) ...
The assessment of stability in cluster analysis is strongly related to the main difficult problem of...
Stability has been considered an important property for evaluating clustering solutions. Nevertheles...
Clustering is a challenging problem in unsupervised learning. In lieu of a gold standard, stability ...
We introduce a general technique for making statistical inference from clustering tools applied to g...
In this work, a novel technique to address the problem of cluster validation based on cluster stabil...
© 2015, Classification Society of North America. Because of its deterministic nature, K-means does n...
Segmentation results derived using cluster analysis depend on (1) the structure of the data and (2...
In cluster analysis, selecting the number of clusters is an "ill-posed" problem of crucial importanc...
Abstract:- Clustering is a process of discovering groups of objects such that the objects of the sam...
Over the past few years, the notion of stability in data clustering has received growing attention a...
A popular method for selecting the number of clusters is based on sta-bility arguments: one chooses ...
<p>Maximum likelihood tree with the 48 transmission clusters colored in green. Maximum likelihood tr...
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1983.MICROFICHE COPY AVA...
Cluster validity investigates whether generated clusters are true clusters or due to chance. This is...
Segmentation results derived using cluster analysis depend on (1) the structure of the data and (2) ...
The assessment of stability in cluster analysis is strongly related to the main difficult problem of...
Stability has been considered an important property for evaluating clustering solutions. Nevertheles...
Clustering is a challenging problem in unsupervised learning. In lieu of a gold standard, stability ...
We introduce a general technique for making statistical inference from clustering tools applied to g...
In this work, a novel technique to address the problem of cluster validation based on cluster stabil...
© 2015, Classification Society of North America. Because of its deterministic nature, K-means does n...
Segmentation results derived using cluster analysis depend on (1) the structure of the data and (2...
In cluster analysis, selecting the number of clusters is an "ill-posed" problem of crucial importanc...
Abstract:- Clustering is a process of discovering groups of objects such that the objects of the sam...
Over the past few years, the notion of stability in data clustering has received growing attention a...
A popular method for selecting the number of clusters is based on sta-bility arguments: one chooses ...
<p>Maximum likelihood tree with the 48 transmission clusters colored in green. Maximum likelihood tr...