Internal cluster validity measures (such as the Calinski-Harabasz, Dunn, or Davies-Bouldin indices) are frequently used for selecting appropriate number of partitions a dataset should be split into. In this paper we consider what happens if treat such indices objective functions in unsupervised learning activities. Is optimal grouping with regards to, say, Silhouette index really meaningful? It...

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 ...

Objective: In this study, psychometric qualities of multidimensional perfectionism scale (MPS) were evaluated. Methods: Persian version of perfectionism inventory was completed by 48 adults (24 females and 24 males). The sample was selected by cluster random sampling from Sarcheshmeh adult inhabitants. Reliability of the scale was assessed by calculating Cronbach's alpha coefficient. Then 26...

Clustering algorithm and cluster validity are two highly correlated parts in cluster analysis. In this paper, a novel idea for cluster validity and a clustering algorithm based on the validity index are introduced. A Centroid Ratio is firstly introduced to compare two clustering results. This centroid ratio is then used in prototype-based clustering by introducing a Pair wise Random Swap cluste...

Building predictive models in customer relationship management refers to each stage in the customer’s lifecycle, i.e. the customer acquisition, development and retention. One may notice that the construction of predictive models is more and more frequently accompanied by an attempt to combine analytical tools of the same type or combining various methods. The first approach is named “ensemble m...

In this paper we present a method to detect natural groups in a data set, based on hierarchical clustering. A measure of the meaningfulness of clusters, derived from a background model assuming no class structure in the data, provides a way to compare clusters, and leads to a cluster validity criterion. This criterion is applied to every cluster in the nested structure. While all clusters passi...

Twenty-seven statements make up the Technological Caring Instrument (TCI) which was developed to measure technological caring in nursing. In order to establish a database and its reliability and validity, responses were generated from 193 professional nurses who participated in the study. The results indicate that the TCI has a high internal consistency, construct validity, and sufficient split...

objective: this study aimed to develop and validate a questionnaire to measure waiting anxiety . methods: this was a cross-sectional study. extensive review of literature and expert opinions were used to develop and validate the waiting anxiety questionnaire. a sample of 321 participants was recruited through random cluster sampling (n= 190 iranian men and n= 131 women). the participants filled...

I n t r o d u c t i o n The word "clustering" (unsupervised classification) refers to methods of grouping objects based on some similarity measure between them. Clustering algorithms can be classified into four classes, namely Partitional, Hierarchical, Density-based and Grid-based [8]. Each of these classes has subclasses and different corresponding approaches, e.g., conceptual, fuzzy, selforg...