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 ...

“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...

This paper presents an approach for assessing cluster validity based on similarity knowledge extracted from the Gene Ontology (GO) and databases annotated to the GO. A knowledge-driven cluster validity assessment system for microarray data was implemented. Different methods were applied to measure similarity between yeast genes products based on the GO. This research proposes two methods for ca...

the aim of this study is to analyze the impact of industrial structure of clusters on entrepreneurship in business clusters (case study: valuable metals and precious stones of isfahan's clusters). the relevant population is isfahan clusters of valuable metals and precious stones. number of population is over than 300enterprises. according to the formula of sampling structural equation mode...

In order to understand patterns of respondents on validity and clinical scales, this study analyzed archival Minnesota Multiphasic Personality Inventory 2s (MMPI-2s) produced by 192 women and 14 men who initiated legal claims of ongoing emotional harm related to workplace sexual harassment and discrimination. The MMPI-2s were administered as a part of a comprehensive psychiatric forensic evalua...

Data Clustering is a technique of finding similar characteristics among the data set which are always hidden in nature and grouping them into groups, called as clusters. Different clustering algorithms exhibit different results, since they are very sensitive to the characteristics of original data set especially noise and dimension. The quality of such clustering process determines the purity o...

This paper proposes a hybrid method of image segmentation by using k-means and agglomerative methods of image segmentation. The K-means method is used to find optimum number of clusters with the help gap method and a validity measure. Then this value is used as a limiting value in merge algorithm. The performance of algorithm is measured using a validity index which is measured by two factors. ...

This paper addresses the relationship between the Visual Assessment of cluster Tendency (VAT) algorithm and Dunn’s cluster validity index. We present an analytical comparison in conjunction with numerical examples to demonstrate that the effectiveness of VAT in showing cluster tendency is directly related to Dunn’s index. This analysis is important to understanding the underlying theory of VAT ...

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 ...

BACKGROUND The extant major psychiatric classifications DSM-IV, and ICD-10, are atheoretical and largely descriptive. Although this achieves good reliability, the validity of a medical diagnosis would be greatly enhanced by an understanding of risk factors and clinical manifestations. In an effort to group mental disorders on the basis of aetiology, five clusters have been proposed. This paper ...