A new color image segmentation method combining mean shift and hierarchical clustering algorithm is presented in this paper. The proposed algorithm preprocesses an input image by mean shift algorithm to form segmented regions that preserve the desirable discontinuity characteristics of image. The number of segmented regions, instead of the number of image pixels, is considered as the input data...

The first GC and GC/MS analyses of the essential oils hydrodistilled from dry leaves of Arctostaphylos uva-ursi and Vaccinium vitis-idaea enabled the identification of 338 components in total (90.4 and 91.7% of the total GC peak areas, respectively). Terpenoids, fatty acids, fatty acid- and carotenoid derived compounds were predominant in the two samples. Both oils were characterized by high re...

The main purpose of this study was to further the understanding of schizotypy by investigating which of two schizotypy models best describes the construct. The quasi-dimensional model views schizotypy as related to psychological ill-health, whereas the fully dimensional model views schizotypy as fundamentally neutral. A schizotypy measure, the Oxford–Liverpool Inventory of Feelings and Experien...

The present study utilized hierarchical agglomerative cluster (HAC) analysis to categorize users of a popular, web-based computer-assisted pronunciation training (CAPT) program into user types using activity log data. Results indicate an optimal grouping of four types: Reluctant, Point-focused, Optimal, and Engaged. Clustering was determined by aggregate data on seven indicator variables of mix...

High dimensionality of text can be a deterrent in applying complex learners such as Support Vector Machines to the task of text classification. Feature clustering is a powerful alternative to feature selection for reducing the dimensionality of text data. In this paper we propose a new informationtheoretic divisive algorithm for feature/word clustering and apply it to text classification. Exist...

Equitable regional development is still difficult to implement. Many interests in determining priorities make it realize the main goals of national development. Political and underutilization statistical data are often other obstacles carrying out This study aims classify be used analyse existing inequalities. Gross domestic income (GRDP) this study. The researcher hierarchical agglomerative cl...

Machine learning techniques are ever prevalent as datasets continue to grow daily. Associative classification (AC), which combines and association rule mining algorithms, plays an important role in understanding big that generate a large number of rules. Clustering, on the other hand, can contribute by reducing space produce compact models. The above-mentioned facts were main motivation for thi...

this paper presents results of hydro-chemical processes controlling groundwater chemical composition, using an integrated application of hierarchical cluster analysis and factor analysis of a major ion data set of groundwater from mighan playa aquifer. cluster analysis classified samples into four clusters(a, b, c and d) according to their dominant chemical composition: cluster a (dominant comp...

Conceptual clustering is a discovery process that groups a set of data in the way that the intra-cluster similarity is maximized and the inter-cluster similarity is minimized. Traditional clustering algorithms employ some measure of distance between data points in n-dimensional space. However, not all data types can be represented in a metric space, therefore no natural distance function is ava...

Fragaria nilgerrensis Schlecht. (wild strawberry) is widely distributed in Southwest China, characterized by stress tolerance and the fruits of a notable peach aroma. So far there only limited knowledge variability genetic structure this species. Using AFLP markers, we investigated 37 plants F. sampled six main mountain areas Sichuan Province analyzed their structure. Genetic similarity accordi...