In order to find an optimal fuzzy cluster scheme for proximity data, where just pairwise distances among objects are given, two conditions are necessary: A good cluster validity function, which can be applied to proximity data for evaluation of the goodness of cluster schemes for varying number of clusters; a good cluster algorithm that can deal with proximity data and produce an optimal soluti...

conclusions the used instrument for measuring domestic violence had desirable validity and reliability and can be used as a suitable instrument in health and social researches in the local population. results the self-administered instrument was completed by 334 women. the cfa and efa methods confirmed embedding items and the three-factor structure of the instrument including psychological, phy...

In this study a new internal clustering validation index is proposed. It based on measure of the uniformity data in clusters. uses local density each cluster, particular, normalized variability within clusters to find ideal partition. The validity allows it capture spatial pattern and obtain right number an automatic way. This approach, unlike traditional one that usually identifies well-separa...

This paper provides non-experimental field evidence on positive and negative worker reciprocity. We analyze the performance reactions of professional workers to fair and unfair wage allocations in their natural environment. The objects of interest are professional soccer players in the German Bundesliga. This environment enables us to circumvent the main problems of observational studies on rec...

We present V-measure, an external entropybased cluster evaluation measure. Vmeasure provides an elegant solution to many problems that affect previously defined cluster evaluation measures including 1) dependence on clustering algorithm or data set, 2) the “problem of matching”, where the clustering of only a portion of data points are evaluated and 3) accurate evaluation and combination of two...

Cluster analysis is used to explore structure in unlabeled data sets in a wide range of applications. An important part of cluster analysis is validating the quality of computationally obtained clusters. A large number of different internal indices have been developed for validation in the offline setting. However, this concept has not been extended to the online setting. A key challenge is to ...

Many stability measures, such as Normalized Mutual Information (NMI), have been proposed to validate a set of partitionings. It is highly possible that a set of partitionings may contain one (or more) high quality cluster(s) but is still adjudged a bad cluster by a stability measure, and as a result, is completely neglected. Inspired by evaluation approaches measuring the efficacy of a set of p...

Gaining confidence that a clustering algorithm has produced meaningful results and not an accident of its usually heuristic optimization is central to data mining. This is the issue of cluster validity. We propose here a method by which proximity graphs are used to effectively detect border points and measure the margin between clusters. With analysis of boundary situation, we design a framewor...

Cluster analysis finds its place in many applications especially in data analysis, image processing, pattern recognition, market research by grouping customers based on purchasing pattern, classifying documents on web for information discovery, outlier detection applications and act as a tool to gain insight into the distribution of data to observe characteristics of each cluster. This ensures ...

A review of some popular fuzzy cluster validity indices is given. An index that is based on the generalization of silhouettes to fuzzy partitions is compared with the reviewed indices in conjunction with fuzzy c-means clustering.