This paper proposes a hybrid method of image segmentation by using k-means and agglomerative methods of image segmentation. The K-means method is used to find optimum number of clusters with the help gap method and a validity measure. Then this value is used as a limiting value in merge algorithm. The performance of algorithm is measured using a validity index which is measured by two factors. ...

We review two clustering algorithms (hard c-means and single linkage) and three indexes of crisp cluster validity (Hubert's statistics, the Davies-Bouldin index, and Dunn's index). We illustrate two deficiencies of Dunn's index which make it overly sensitive to noisy clusters and propose several generalizations of it that are not as brittle to outliers in the clusters. Our numerical examples sh...

The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies–Bouldi...

In this paper, we propose a GA-based unsupervised clustering technique that selects cluster centers directly from the data set, allowing it to speed up the fitness evaluation by constructing a look-up table in advance, saving the distances between all pairs of data points, and by using binary representation rather than string representation to encode a variable number of cluster centers. More e...

In this paper, the authors study the parameter sensitivity of the technique of particles warm optimization (PSO) for the clustering of data, in particular the text. They experienced the PSO parameters by varying within a range of research and we noted the best result of clustering based on three measures of assessment, internal, which is the index of Davies and Bouldin and two external based on...

Figure 2.3. Envelope realizations: (a) the realized envelopes from 0-400 seconds with the estimated and true transition points, (b) the local mean of the envelope realizations in (a), and (c) the local entropy of the envelope realizations in (a). Details of the 40-60 seconds are shown in (d)(e) vi Figure 2.4. (a) Feature representations of the segments from the simulation. (b) The elbow phenome...

Data mining (DM) methodology has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the data used by researchers. This study has proposed a new procedure, based on expanded RFM model by including one additional parameter, joining WRFM-based method to K-means algorithm applied in DM with K-optimum according to Davies– Bouldin In...

A new method for finding fuzzy information granules from multivariate data through a gravitational inspired clustering algorithm is proposed in this paper. The proposed algorithm incorporates the theory of granular computing, which adapts the cluster size with respect to the context of the given data. Via an inspiration in Newton’s law of universal gravitation, both conditions of clustering sim...

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 ...