Cluster Analysis of Multicriteria-Classified Wheeled Armored Vehicles

Cluster analysis was performed on the data representing the defense of 32 wheeled armored vehicles based on a Multi Criteria Decision Support model. The number of clusters was determined by nonhierarchical clustering, while the vehicles were assigned to a cluster by non‑hierarchical clustering. The number of clusters are either three or eight. For three assumed clusters, the BTR‑type, the equipment designed before and after 2000 were grouped together, while for eight assumed clusters, these groups split into subgroups. Each subgroup consist of vehicles with similar defense, while the distinction between the subgroups could be made on the basis of modernization, the evolution of defense techniques in time.

Genetic diversity of muskmelon using multivariate technique

An experiment was conducted at the experimental farm of Plant Genetic Resources Centre (PGRC), Bangladesh Agricultural Research Institute (BARI), Gazipur in 2011 to estimate genetic diversity through multivariate technique. Based on multivariate analysis and application of covariance matrix for nonhierarchical clustering, 64 genotypes of muskmelon were grouped into six clusters to indicate the existence of considerable diversity among the genotypes. The cluster IV was consisted of single genotypes (BD2303). The highest number of genotypes possessed in Cluster I. The first principal axis largely accounted for the variation among the genotypes which alone contributed 25.65% of the variations. The highest inter genotypic distance (2.878) was observed between the genotypes BD2303 and BD2313 followed by the genotypes BD2303 and BD2314 (2.808).The highest intra cluster distance was computed for cluster III (0.839) followed by cluster I (0.751). Cluster VI showed the least intra cluster distance which indicated that the genotypes in this cluster were more or less homogeneous. The inter cluster distances were larger than the intra cluster distances suggesting wider genetic diversity among the genotypes of different clusters. Cluster mean pointed out the heavier fruit in cluster IV (2533.3g). The size of this cluster was also far different from all other clusters. Similarly, the highest total fruit weight per plant was found in cluster IV (13.5 kg) which was also far different from other clusters. So it revealed that genotypes of this cluster could be used for developing high yielding variety. Cluster VI showed the highest brix reading (5.6%). Therefore, the genotypes of this cluster could be used for the development of sweet muskmelon variety. Hybridization between the genotypes of cluster IV and those of cluster VI could develop high yielding sweet muskmelon variety(s).Bangladesh J. Agril. Res. 41(2): 273-286, June 2016

COMPARATIVE STUDY OF CLUSTERING ALGORITHMS IN ORDER TO VIRTUAL HISTOLOGY (VH) IMAGE SEGMENTATION

Atherosclerosis is the deadliest type of heart disease caused by soft or “vulnerable” plaque (VP) formation in the coronary arteries. Recently, Virtual Histology (VH) has been proposed based on spectral analysis of Intravascular Ultrasound (IVUS) provides color code of coronary tissue maps. Based on pathophysiological studies, obtaining information about existence and extension of confluent pool’s component inside plaque is important. In addition, plaque components’ localization respect to the luminal border has major role in determining plaque vulnerability and plaque–stent interaction. Computational methods were applied to prognostic the pattern's structure of each component inside the plaque. The first step for post-processing of VH methodology to get further information of geometrical features is segmentation or decomposition. The medical imaging segmentation field has developed to assist cardiologist and radiologists and reduce human error in recent years as well. To perform color image clustering, several strategies can be applied which include traditional hierarchical and nonhierarchical. In this paper, we applied and compared four nonhierarchical clustering methods consists of Fuzzy C-means (FCM), Intuitionistic Fuzzy C-means (IFCM), K-means and SOM artificial neural networks in order to automate segmentation of the VH-IVUS images.

Cluster-Based Pavement Deterioration Models for Low-Volume Rural Roads

The management of low-volume rural roads in developing countries presents a range of challenges to road designers and managers. Rural roads comprise over 85 percent of the road network in India. The present study aims at development of deterioration models for the optimum maintenance management of the rural roads under a rural road programme namely Pradhan Mantri Gram Sadak Yojana (PMGSY) in India. Visual condition survey along the selected low-volume rural roads considers parameters like condition of shoulders, drainage features, cross-drainage structures, and camber, and pavement distresses, namely, potholes, crack area, and edge break, are collected for a period of three years. The deterioration models have a significant role in the pavement maintenance management system. However, the performance of a pavement depends on several factors. Cluster analysis can be used to group the pavement sections so that the performance of pavements in different clusters can be studied. Nonhierarchical clustering technique of k-means clustering was considered. Separate deterioration models have been developed for each of the clusters. A comparison of the models developed with and without clustered sections reveals that the clustering of pavement sections are preferred for the efficient rural road maintenance management.

Pooling of low flow regimes using cluster and principal component analysis

Pooling of low flow regimes using cluster and principal component analysisThis article deals with the regionalization of low flow regimes lower than Q95 in Slovakia. For the regionalization of 219 small and medium-sized catchments, we used a catchment area running from 4 to 500 km2 and observation periods longer than 20 years. The relative frequency of low flows lower than Q95 was calculated. For the regionalization, the nonhierarchical clustering K-means method was applied. The Silhouette coefficient was used to determine the right number of clusters. The principal components were found from the pooling variables on the principal components. The K-means clustering method was applied. Next, we compared the differences between the two methods of pooling data into regional types. The results were compared using an association coefficient.

Investigation of End-Milling Condition Decision Methodology Based on Data Mining for Tool Catalog Database

Data mining supports decision making about reasonable end-milling conditions. Our research objective is to excavate new knowledge with mining effect by applying data mining techniques to a tool catalog. We use hierarchical and nonhierarchical clustering data mining with catalog data by applying multiple regression analysis and focusing on the catalog data shape element. We visually grouped end-mills on the basis of tool shape, considering the ratio of tool shape dimensions, by employing the K-means method. We found that factors related to blade length and full length ratio are effective in for making end-milling condition decisions. These factors have not previously been singled out through background knowledge or expert knowledge, but they were noticed as a data mining effect.