Clustering is one of the most well known types of unsupervised learning. Evaluating the quality of results and determining the number of clusters in data is an important issue. Most current validity indices only cover a subset of important aspects of clusters. Moreover, these indices are relevant only for data sets containing at least two clusters. In this paper, a new bounded index for cluster...

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal...

OBJECTIVE We developed a reliable and valid fidelity measure for use in research on Ayres Sensory Integration (ASI) intervention. METHOD We designed a fidelity instrument to measure structural and process aspects of ASI intervention. Because scoring of process involves subjectivity, we conducted a series of reliability and validity studies on the process section. Raters were trained to score ...

We investigate the determinants of US credit union capital-to-assets ratios, before and after the implementation of the current capital adequacy regulatory framework in 2000. Capitalization varies pro-cyclically, and credit unions classified as adequately capitalized or below followed a faster adjustment path than well capitalized credit unions. Credit unions managed their capital more actively...

Document clustering aims to automatically group related document into clusters. If two documents are close to each other in the original document space, they are grouped into the same cluster. If the two documents are far away from each other in the original document space, they tend to be grouped into different cluster. The classical clustering algorithms assign each data to exactly one cluste...

Fraud is an unauthorized activity taking place in electronic payments systems, but these are treated as illegal activities. Fraud detection methods are continuously developed to defend criminals in adapting to their strategies. Fraud can be identified quickly and easily through fraud detection techniques. In this paper, clustering approach is used for credit card fraud detection. Data is genera...

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 ...

Fraud is an unauthorized activity taking place in electronic payments systems, but these are treated as illegal activities. Fraud detection methods are continuously developed to defend criminals in adapting to their strategies. Fraud can be identified quickly and easily through fraud detection techniques. In this paper, clustering approach is used for credit card fraud detection. Data is genera...

In this paper, we develop three measures of association between concepts and features from three measures of category structure preference. These measures are total cue validity, feature possession score, and category utility. We compare these measures experimentally using stimuli from the Leuven Natural Concept Database (de Deyne et al., 2008). We find the measure developed from feature posses...

Much attention is being given to the incorporation of constraints into data clustering, mainly expressed in the form of must-link and cannot-link constraints between pairs of domain objects. However, its inclusion in the important clustering validation process was so far disregarded. In this work, we integrate the use of constraints in clustering validation. We propose three approaches to accom...