Executive dyscontrol of episodic verbal learning and memory secondary to prefrontostriatal circuit neuropathophysiology is a common feature of HIV-1 infection. Prior research indicates that standard clinical learning and recall indexes from Hopkins Verbal Learning Test-Revised (HVLT-R) are among the most sensitive indicators of HIV-associated neurocognitive disorders. Emerging data support the ...

Cluster validity has been mainly used to evaluate the quality of individual clusters, and compare whole partitions resulting from different or same (using different parameters) clustering algorithms [13]. However, depending on the application, the demands for a validity measure may differ, inducing the necessity of introducing new measures which will suit to the problem under investigation. We ...

Interpretation of aerial images is an important task in various applications. Image segmentation can be viewed as the essential step for extracting information from aerial images. Among many developed segmentation methods, the technique of clustering has been extensively investigated and used. However, determining the number of clusters in an image is inherently a difficult problem, especially ...

Clustering (or cluster analysis) has been used widely in pattern recognition, image processing, and data analysis. It aims to organize a collection of data items into c clusters, such that items within a cluster are more similar to each other than they are items in the other clusters. The number of clusters c is the most important parameter, in the sense that the remaining parameters have less ...

Based on the basic theory of fuzzy set, this paper suggests the notion of FCM fuzzy set, which is subject to the constraint condition of fuzzy c-means clustering algorithm. The cluster fuzzy degree and the lattice degree of approaching for the FCM fuzzy set are presented, and their functions in the validation process of fuzzy clustering are deeply analyzed. A new cluster validity index is propo...

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 cluster validity indices, namely Davies-Bouldin index, Dunn’s index, Calinski-Harabasz index, and a recently developed index I . Based on a relation between the index I and the Dunn’s index, a lower bound of the value...

This paper presents an algorithm for generating scale-free networks with adjustable clustering coefficient. The algorithm is based on a random walk procedure combined with a triangle generation scheme which takes into account genetic factors; this way, preferential attachment and clustering control are implemented using only local information. Simulations are presented which support the validit...

“The statistical problem of testing cluster validity is essentially unsolved” [5]. We translate the issue of gaining credibility on the output of un-supervised learning algorithms to the supervised learning case. We introduce a notion of instance easiness to supervised learning and link the validity of a clustering to how its output constitutes an easy instance for supervised learning. Our noti...

The structural characteristics of clusters are investigated in the partitioning process. Two partition functions, which show opposite properties around the optimal cluster number, are found and a new cluster validity index is presented based on the combination of these functions. Some properties of the index function are discussed and numerical examples are presented. key words: clustering, val...

One of the most challenging aspects of clustering is validation, which is the objective and quantitative assessment of clustering results. A number of different relative validity criteria have been proposed for the validation of globular, clusters. Not all data, however, are composed of globular clusters. Density-based clustering algorithms seek partitions with high density areas of points (clu...