Cluster validity indexes can be used to evaluate the fitness of data partitions produced by a
clustering algorithm. Validity indexes are usually independent of clustering algorithms …

Cluster validation is an important part of any cluster analysis. External measures such as
entropy, purity and mutual information are often used to evaluate K-means clustering …

Neural networks are very effective when trained on large datasets for a large number of
iterations. However, when they are trained on non-stationary streams of data and in an …

Many cluster similarity indices are used to evaluate clustering algorithms, and choosing the
best one for a particular task remains an open problem. We demonstrate that this problem is …

Comparing two clustering results of a data set is a challenging task in cluster analysis. Many
external validity measures have been proposed in the literature. A good measure should be …

Clustering evaluation plays an important role in unsupervised learning systems, as it is often
necessary to automatically quantify the quality of generated cluster configurations. This is …

In this article, we evaluate the performance of three clustering algorithms, hard K-Means,
single linkage, and a simulated annealing (SA) based technique, in conjunction with four …

Many different relative clustering validity criteria exist that are very useful in practice as
quantitative measures for evaluating the quality of data partitions, and new criteria have still …

Clustering is a mostly unsupervised procedure and the majority of the clustering algorithms
depend on certain assumptions in order to define the subgroups present in a data set …

In real life, availability of correctly labeled data and handling of categorical data are often
acknowledged as two major challenges in pattern analysis. Thus, clustering techniques are …