Simultaneous selection of the number of clusters and of a relevant subset of features is part of data mining challenges. A new approach is proposed to address this difficult issue. It takes benefits of both two-levels clustering approaches and wrapper features selection algorithms. On the one hands, the former enhances the robustness to outliers and to reduce the running time of the algorithm. ...

This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data sources, three types of comparisons of resulting c...

Interpretation of aerial images is an important task in various applications. Image segmentation can be viewed as the essential step for extracting information from aerial images. Among many developed segmentation methods, the technique of clustering has been extensively investigated and used. However, determining the number of clusters in an image is inherently a difficult problem, especially ...

This paper suggests the nonlinear parameter of self-similarity as a novel feature to be employed in wavelet packet based voice signal analysis. Two groups of normal and pathological voice signals have been decomposed using wavelet packets. Next, self similar characteristics of reconstructed signals in each node have been calculated. Consequently, discrimination ability of each node has been obt...

We present a multiobjective genetic clustering approach, in which data points are assigned to clusters based on new line symmetry distance. The proposed algorithm is called multiobjective line symmetry based genetic clustering (MOLGC). Two objective functions, first the Davies-Bouldin (DB) index and second the line symmetry distance based objective functions, are used. The proposed algorithm ev...

In view of the fact that there are disadvantages in that the class number must be determined in advance, the value of learning rates are hard to fix, etc., when using traditional competitive neural networks (CNNs) in electronic noses (E-noses), an optimized CNN method was presented. The optimized CNN was established on the basis of the optimum class number of samples according to the changes of...

This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the D...

In this article, we evaluate the performance of three clustering algorithms, hard K-Means, single linkage, and a simulated annealing (SA) based technique, in conjunction with four cluster validity indices, namely Davies-Bouldin index, Dunn’s index, Calinski-Harabasz index, and a recently developed index I . Based on a relation between the index I and the Dunn’s index, a lower bound of the value...