<p>Cluster analysis was used to detect the presence of relatively homogeneous groups of calls. Silhouette Information was computed as a method of cluster interpretation and validation; the highest average silhouette classification score (0.62) was achieved by a four-groups solution based on DC, duration and maximum fundamental frequencies as input variables.</p
<p>Decision making process regarding ‘optimal’ number of clusters was performed on the basis of this...
<p>Agglomerative hierarchical cluster analysis for the whole data set to define the natural division...
We investigate the use of silhouette coefficients in cluster analysis for speaker diarisation, with ...
AbstractA new graphical display is proposed for partitioning techniques. Each cluster is represented...
<p>Parameters used: log-transformed Duration, Median F0, Standard Deviation F0, Ampvar, NHR, Jitter ...
<p>An obvious knee point (K = 140) is selected as the number of clusters...
(a) Dendrogram illustrating the results of hierarchical clustering. (b) Silhouette coefficients calc...
Grouping the objects based on their similarities is an important common task in machine learning app...
Clustering plays a fundamental role in Machine Learning. With clustering we refer to the problem of ...
The Average Silhouette Width (ASW) is a popular cluster validation index to estimate the number of c...
Silhouette analysis for K-Means clustering on 30 provinces with n_clusters = 3,4,5.</p
Silhouette is one of the most popular and effective internal measures for the evaluation of clusteri...
<p>The plots represent the comparison of five different methods of finding optimum number of cluster...
Finding compact and well-separated clusters in data sets is a challenging task. Most clustering algo...
Cluster analysis is the search for groups of alike instances in the data. The two major problems in ...
<p>Decision making process regarding ‘optimal’ number of clusters was performed on the basis of this...
<p>Agglomerative hierarchical cluster analysis for the whole data set to define the natural division...
We investigate the use of silhouette coefficients in cluster analysis for speaker diarisation, with ...
AbstractA new graphical display is proposed for partitioning techniques. Each cluster is represented...
<p>Parameters used: log-transformed Duration, Median F0, Standard Deviation F0, Ampvar, NHR, Jitter ...
<p>An obvious knee point (K = 140) is selected as the number of clusters...
(a) Dendrogram illustrating the results of hierarchical clustering. (b) Silhouette coefficients calc...
Grouping the objects based on their similarities is an important common task in machine learning app...
Clustering plays a fundamental role in Machine Learning. With clustering we refer to the problem of ...
The Average Silhouette Width (ASW) is a popular cluster validation index to estimate the number of c...
Silhouette analysis for K-Means clustering on 30 provinces with n_clusters = 3,4,5.</p
Silhouette is one of the most popular and effective internal measures for the evaluation of clusteri...
<p>The plots represent the comparison of five different methods of finding optimum number of cluster...
Finding compact and well-separated clusters in data sets is a challenging task. Most clustering algo...
Cluster analysis is the search for groups of alike instances in the data. The two major problems in ...
<p>Decision making process regarding ‘optimal’ number of clusters was performed on the basis of this...
<p>Agglomerative hierarchical cluster analysis for the whole data set to define the natural division...
We investigate the use of silhouette coefficients in cluster analysis for speaker diarisation, with ...