In this paper, the utility of credibilistic critical values in crisp conversion of fuzzy data sets is considered. Conversion of this type becomes essential mainly when clustering of fuzzy data sets is carried out. In this paper performance of two popular clustering algorithms namely Fuzzy c–means and Fuzzy c–medoids algorithms are evaluated under credibilistic critical value crisp conversion is...

The kMeans clustering algorithm is an old algorithm that has been intensely researched owing to its ease and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in exploratory data analysis. This paper presents results of the experimental study of different approaches to kMeans clustering, thereby comparing results on different datasets using Original k-Mean...

In this paper, we modify the mountain method and then create a modified mountain clustering algorithm. The proposed algorithm can automatically estimate the parameters in the modified mountain function in accordance with the structure of the data set based on the correlation self-comparison method. This algorithm can also estimate the number of clusters based on the proposed validity index. As ...

It is proposed that the inertial range clustering of small heavy particles in fluid turbulence occurs as a result of the sweep-stick mechanism which causes inertial particles to cluster so as to mimic the clusters of points where the fluid acceleration is perpendicular to the direction of highest contraction between neighboring particles. Direct numerical simulations of inertial particles subje...

This paper is concerned with clustering in demand. We present a discrete choice model of consumption that incorporates habit formation and information exchange among consumers in fixed social networks. We provide an analytical solution to a special case of the model by using technical tools from chemistry and biology. We demonstrate the validity of these results for the general case numerically...

cluML is a new markup language for microarray data clustering and cluster validity assessment. The XML-based format has been designed to address some of the limitations observed in traditional formats, such as inability to store multiple clustering (including biclustering) and validation results within a dataset. cluML is an effective tool to support biomedical knowledge representation in gene ...

Clustering is an unsupervised technique of Data Mining. It means grouping similar objects together and separating the dissimilar ones. Each object in the data set is assigned a class label in the clustering process using a distance measure. This paper has captured the problems that are faced in real when clustering algorithms are implemented .It also considers the most extensively used tools wh...

This paper illustrates some of the problems which can occur in any data set when clustering samples of gene expression profiles. These include a possible high degree of dependence of results on choice of clustering algorithm, further dependence of results on the choices of genes and samples to be included in the clustering (for example, whether or not to include control samples), and difficulty...

Fuzzy modelling has interpretability of the obtained models as a fundamental goal. In this paper a control-oriented local-model fuzzy clustering algorithm will try that local models approximate the linearized plant model on their validity zones. A family of clustering algorithms is presented so that it incorporates some desirable characteristics regarding convexity and smoothness of the final i...

This paper describes the validity-guided fuzzy clustering evaluation for optimal training of localized neural networks (LNNs) used for reassigning time-frequency representations (TFRs). Our experiments show that the validity-guided fuzzy approach ameliorates the difficulty of choosing correct number of clusters and in conjunction with neural network-based processing technique utilizing a hybrid...