Text documents have sparse data spaces and current existing methods of text clustering use symmetry proximity to measure the correlation of documents. In this paper, we propose a novel approach to strengthen the discriminative feature of document objects, which uses asymmetric proximity for text clustering. We present a measure of asymmetric proximity between documents and between clusters. TCU...

Many validity measures and learning algorithms have been applied for quantitatively evaluating the performance of clustering algorithms. However, existing validity measures and clustering evaluation are based on a single realization, and lack of generality. These tasks involve more than one criterion, so it can be modelled as a multiple criteria decision making (MCDM) problem. In this paper, we...

BACKGROUND In order to monitor and ultimately improve the quality of addiction treatment, professional societies, health care systems, and addiction treatment programs must establish clinical practice standards and then operationalize these standards into reliable, valid, and feasible quality measures. Before being implemented, quality measures should undergo tests of validity, including predic...

Background The Yin-Yang is a pivotal concept of traditional East-Asian medicine, however the stability of Yin-Yang temperament in Sasang Personality Questionnaire (SPQ) over time has not been extensively studied. The purpose of this study was to examine the test-retest validity of SPQ with a large number of participants. Methods SPQ test was conducted two times with three months interval in 2...

The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...

Abstract Clustering can be defined as the process of grouping physical or abstract objects into classes of similar objects. It’s an unsupervised learning problem of organizing unlabeled objects into natural groups in such a way objects in the same group is more similar than objects in the different groups. Conventional clustering algorithms cannot handle uncertainty that exists in the real life...

in this study, we considered some competitive learning methods which include hard competitive learning (hcl) and soft competitive learning (scl) with/ without fixed network dimensionality for reliability analysis in microarrays. in order to have a more extensive view, and keeping in mind that competitive learning methods aim at error minimization or entropy maximization (different kinds of func...