scholarly journals Cluster Analysis of Multicriteria-Classified Wheeled Armored Vehicles

Hadmérnök ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. 21-33
Author(s):  
Bence Tóth ◽  
György Gávay
Keyword(s):  
Cluster Analysis ◽  
Decision Support ◽  
Hierarchical Clustering ◽  
Decision Support Model ◽  
Number Of Clusters ◽  
Before And After ◽  
Armored Vehicles ◽  
Nonhierarchical Clustering ◽  
Support Model

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.

scholarly journals Nonhierarchical clustering technique (GROUPER)

1974 ◽  
Vol 6 (4) ◽  
pp. 446-448 ◽  
Author(s):  
Ronald G. Sherwin ◽  
Nien-ling Wayman
Keyword(s):  
Clustering Technique ◽  
Nonhierarchical Clustering

scholarly journals Genetic diversity of muskmelon using multivariate technique

2016 ◽  
Vol 41 (2) ◽  
pp. 273-286
Author(s):  
S Rahman ◽  
MAK Miah ◽  
H Rahman
Keyword(s):  
Genetic Diversity ◽  
Principal Axis ◽  
Agricultural Research ◽  
Plant Genetic Resources ◽  
Fruit Weight ◽  
Agricultural Research Institute ◽  
High Yielding Variety ◽  
Multivariate Technique ◽  
Cluster Distance ◽  
Nonhierarchical Clustering

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

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

2012 ◽  
Vol 6 (1) ◽  
pp. 61-74 ◽  
Author(s):  
Hiroyuki Kodama ◽  
◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
Keiji Ogawa ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Expert Knowledge ◽  
End Milling ◽  
New Knowledge ◽  
Tool Shape ◽  
End Mills ◽  
Milling Condition ◽  
Clustering Data ◽  
Nonhierarchical Clustering

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.

BINCLUS: Nonhierarchical Clustering of Binary Data

1986 ◽  
Vol 21 (2) ◽  
pp. 201-227 ◽  
Author(s):  
Norman Cliff ◽  
Douglas J. McCormick ◽  
Judith L. Zatkin ◽  
Robert A. Cudeck ◽  
Linda M. Collins
Keyword(s):  
Binary Data ◽  
Nonhierarchical Clustering
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