The Adjusted Rand Index (ARI) is frequently used in cluster validation since it is a measure of agreement between two partitions: one given by the clustering process and the other defined by external criteria. In this paper we investigate the usability of this clustering validation measure in supervised classification problems by two different approaches: as a performance measure and in feature...

Evaluation of clustering results (or cluster validation) is an important and necessary step in cluster analysis, but it is often time-consuming and complicated work. We present a visual cluster validation tool, the Cluster Validity Analysis Platform (CVAP), to facilitate cluster validation. The CVAP provides necessary methods (e.g., many validity indices, several clustering algorithms and proce...

background: alexithymia as a cluster of cognitive and affective deficits has been studied for its ability to predict a variety of psychological disorders. given its clinical importance, various self-report questionnaires have been developed to measure alexithymia. the aim of this study was to determine the psychometric characteristics of persian version of the toronto alexithymia scale-20 (ftas...

The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types some of which cover significantly large areas, while some (e.g., bridges and roads) occupy relatively much smaller regions. Automatically detecting regions or clusters of such widely varying sizes pr...

Case-base Reasoning (C great attention in the last allows user to store, sha stored inside the system solving experiences/cases solve unknown problems knowledge. When the ca very difficulty to maintai when similar cases have as redundant cases, co cases, subsumed cases exist in the case-base. Th maintenance (CBM) prob a method to improve the p classification of cases in using a learned global f...

In this paper, a cluster validity concept from an unsupervised to a supervised manner is presented. Most cluster validity criterions were established in an unsupervised manner, although many clustering methods performed in supervised and semi-supervised environments that used context information and performance results of the model. Context-based clustering methods can divide the input spaces u...