A simple and effective fuzzy clustering approach is presented for fuzzy modeling from industrial data. In this approach, fuzzy clustering is implemented in two phases: data compression by a self-organizing network, and fuzzy partitioning via fuzzy cmeans clustering associated with a proposed cluster validity measure. The approach is used to extract fuzzy models from data and find out the optima...

This paper presents a novel hybrid data clustering algorithm based on parameter adaptive harmony search algorithm. The recently developed parameter adaptive harmony search algorithm (PAHS) is used to refine the cluster centers, which are further used in initializing Expectation-Maximization clustering algorithm. The optimal number of clusters are determined through four well-known cluster valid...

In this paper, five clustering techniques (k-means, ISODATA, merging, splitting and mean shift techniques) used for colour image segmentation are presented. Two heuristic evaluation methods (cluster validity measure VM and quality function Q) are applied. We show that evaluation functions VM and Q can be very helpful in search of best segmentation results. The best results came from k-means, me...

The K-Means clustering is by far the most widely used method for discovering clusters in data. It has a good performance on the data with compact super-sphere distributions, but tends to fail in the data organized in more complex and unknown shapes. In this paper, we analyze in detail the characteristic property of data clustering and propose a novel dissimilarity measure, named density-sensiti...

Data clustering describes a set of frequently employed techniques in exploratory data analysis to extract "natural" group structure in data. Such groupings need to be validated to separate the signal in the data from spurious structure. In this context, finding an appropriate number of clusters is a particularly important model selection question. We introduce a measure of cluster stability to ...

OBJECTIVE To determine the content, structural, and construct validity of the Dutch version of the Neck Disability Index (NDI). STUDY DESIGN AND SETTING To assess content validity, 11 neck pain experts and 10 patients commented on the construct, comprehensiveness, and relevance of the NDI. Structural validity was assessed by item factor analysis (FA) and item response theory modeling using th...

This paper focuses on estimating clustering validity by using logistic regression. For many applications it might be important to estimate the quality of the clustering, e.g. in case of speech segments’ clustering, make a decision whether to use the clustered data for speaker verification. In the case of short segments speakers clustering, the common criteria for cluster validity are average cl...