• Journal of Engineering Education Research
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• v.8 no.1
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• pp.31-45
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• 2005

In this paper, selecting survey items was performed using three clustering methods: factor analysis, fuzzy c-Means algorithm and cluster analysis. The methods were used to extract key items from various questionnaires. The key item represents several similar questionnaires that form a cluster. Test survey was made of 120 items obtained from several surveys and it was answered by 646 students from 4 universities. Each item contains 6 choices. Applying the clustering method chose 25 items which is reduced from the original 120 items. The results yielded by three methods are very similar.

• KCID journal
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• v.18 no.2
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• pp.66-75
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• 2011

Multivariate statistical analysis was applied to 110 dam heightening reservoir to classify the building conditions for waterfront centered around cultivated area using data of land cover, landscape, additional water quantity, local economic, tourism resources, and accessibility related variables. Five factors were extracted through factor analysis based on eigen value criteria of more than one. These five factors together account for 68.2% of the total variance. Characteristics of five factors for the downstream of dam heightening reservoirs are building conditions of waterfront, economic conditions, additional water quantity, eco-tours, and accessibility of tourism resources respectively. Five clusters were classified through cluster analysis based on factor score. The classified result shows that third cluster has remunerative terms for building waterfront.

• Journal of the Korean Home Economics Association
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• v.42 no.11
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• pp.85-107
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• 2004

This study classified figure types of adult males into several kinds of shape to provide fundamental data for their clothing sizing system. The subjects were 1496 men aged between 20 and 60 years old. Data were analyzed by factor analysis, cluster analysis and discriminant analysis. The results were as follows 1. For the result of the interview, the data were grouped into three age brackets: 20-35,31-45 and 41-60 years. 2. Factor analysis using values, which were measurements divided by either weight or height, was carried out to extract factors which characterize the various figures. fve factors to determine the figure types were extracted. 3. Cluster analysis using factor scores was carried out to categorize the figure types within the age groups. Figure types, describing shoulder angie and body shape, were categorized into 3 per age group. 4. Stepwise discriminant analysis w3s used to ensure that these clusters could be utilized with appropriate hit ratio. The hit ratio for each age group was around 80%.

• Fashion & Textile Research Journal
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• v.4 no.1
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• pp.71-78
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• 2002

The purpose of this study was to classify body types of 800 female junior and high school students based on factor analysis, cluster analysis, and correspondence analysis of 15 photometric measurements of the subjects' lateral body lines. The results are as follows: The skeleton structure, which was represented the back bending by the photometric measurement, was already formed at age of 12 or 13, while the cervical skeleton, the size and inclination of the shoulder, and the degree of the lateral inclination of the upper body and the buttocks continued to grow by the age of 16. A factor analysis of the photometric measurement resulted in the sampling which determined the degree of the back bending, front-bust angle, and lateral view as well as the sampling whose factor represented the cervical inclination. A cluster analysis of the photometric measurement resulted in the four types of classification: Type 1, the straight type comprising 13.3% of the whole population; Type 2, the bent-forward type comprising 39.5%; Type 3, the lean-back type comprising 27.4%; and Type 4, the swayback type comprising 19.1%. Accordingly, the bent-forward type was found to be the dominant type among the four lateral body types.

• Agribusiness and Information Management
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• v.4 no.2
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• pp.1-13
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• 2012

This study aims to subdivide consumers by attributes determined mainly by consumers of fresh ginseng. It is to compare and analyze the characteristics by cluster, and to deduce the implications on distribution and marketing. For this study, a survey was conducted targeting 250 consumers of fresh ginseng. The factors were deduced through performing the exploratory factor analysis on the results of the survey, and the consumers of fresh ginseng were classified through cluster analysis. As a result of the study, the attributes considered for the purchase of fresh ginseng were condensed to the three factors: physical characteristic factor, safety factor, and cultivation indication information factor. With these as the standard, the consumers of fresh ginseng were subdivided into the three clusters: safety-oriented consumption type, label-centered consumption type, and high involvement consumption type. It was found that there were differences in demographic characteristics and attributes considered for purchase of fresh ginseng by cluster analysis. This study suggests the implications for revitalization of the fresh ginseng industry by subdividing consumers of fresh ginseng and suggesting the characteristics by cluster.

• Fashion & Textile Research Journal
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• v.2 no.4
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• pp.300-307
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• 2000

The purpose of this study was to classify and analyze the body type of obese boys who are school year for 9 to 11 years. A total of 49 body measurements were collected for this study including both direct and indirect measurements (33 variables from the direct anthropometric data, 16 variables from the indirect anthropometric data). Data from anthropometric measurement were analyzed using descriptive, factor and cluster analysis. The obese boys investigated in this project were in the category of average height 146.31 cm, weight 52.17 kg. The values for the study were compared to values from the National Anthropometric Survey of Korea. As the result of factor analysis for the classification of body type, 6 factors were extracted from all items. The first was transverse size of upper body, also, the second was its longitudinal size. The third was thicknesses of front and back at side views. The fourth was shoulder shape. The fifth was upper body angle factor. The sixth was upper body length. The body form was classified with cluster analysis, using factor score. The shape of the body was classified in 3 types.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.23-38
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• 2007

Corporations adopt a segmentation depends on the existence of target variables, in general. In this paper, for the case of no target variables, a strategy through segmentation is proposed for corporations having a special status based on the management index. In case of segmentation using cluster analysis, however, if one classify according to many variables then he will be in face of difficulties in characterizing. Therefore, after extracting representative factors by factor analysis, a segmentation method through 2 step cluster analysis is employed on the basis of these representative factors. As a result, six segmentation groups are found and the resulting strategy is proposed which strengthens prominent factors and makes up defective factors for each group.

• Korean Journal of Human Ecology
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• v.12 no.4
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• pp.559-570
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• 2003

The focus of this research was concerned with studying lower body type for Korean adult males. To understand the features of men's lower body required to organize an optimal sizing system for men's tight-fit slacks, information from the measuring values based on research on the physical standard of the nation(l997) were summarized; in addition, a factor analysis and a cluster analysis among multivariate analyses were performed. In terms of the need for appropriate fit in motorcycle wear, basic lower body parts applying to each item had to be taken into consideration to enhance sizing suitability. In the case of the length of the lower limbs, the inclusion of protectors plays an important role in affecting sizing. The factors related to the lower body were defined as follows: Factor 1 was the vertical dimension of the lower body, Factor 2 was the horizontal dimension of waist and hip, Factor 3 was the horizontal dimension of the lower limbs, Factor 4 was the length of hips, and Factor 5 was the ankle height. The lower body part was also divided into 3 clusters. Cluster 1 referred to the smallest stature and skeleton structure among the 3 body types. Cluster 2 represented the biggest stature, with a thin lower limbs body type. Cluster 3 represented a medium stature but with a large skeleton structure of lower limbs, a muscular type. In conclusion, Cluster 2 appeared most in the 20s age groups, but Cluster 3 appeared most in each of the 30s, 40's, and 50's age groups.

• The Research Journal of the Costume Culture
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• v.14 no.3 s.62
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• pp.366-380
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• 2006

The purpose of this study was to examine the effect of shopping orientations on clothing purchasing behavior according to residence. The data was obtained from questionnaires filled out by 530 females living in Seoul and Jecheon. For data analysis, factor analysis, ANOVA, t-test, Cronbach's $\alpha$, Duncan's multiple range test and cluster analysis were used. For shopping orientation, five factors of orientation were found and labeled as hedonism, brand and store loyalty, conformity, economy and rationality factor. Based on five shopping orientation factors, women were classified into five clusters(self-centered and rational, recreational, economy and shopping low involvement, economical and conformative cluster). Each cluster showed significantly different clothing purchasing behaviors (problem recognition, information search and evaluative criteria) and had different demographic variables(age, income, marital status and school career). Finally, residence and shopping orientations have influenced various clothing purchasing behavior. In conclusion, residence was the important factor which influenced on shopping orientation and clothing purchasing behavior.

• Journal of the Korean Society of Clothing and Textiles
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• v.19 no.2
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• pp.366-379
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• 1995

This study was performed to provide fundamental data on middle-aged women's upper torso by classifying the upper torso somatotype and analyzing the characteristics of their somatotype. Factor analysis of principal component model was used to 38 directly measured items, and cluster analysis was applied for classification of upper torso forms. Seven factors were extracted from the factor analysis. The first factor represented the items of circumference, breadth, and depth which were related with body size. On the basis of the cluster analysis using factor scores trom factor analysis as being independent variables, the subjects were classified into three groups. Three dress forms were constructed according to the characteristics for each somatotype of subjects, the three-dimensional characteristics of somatotype were analyzied by the moire pattern and horizontal section map of proposed dress forms. Wearing test by moire topography was used to evaluate wearing outline, the ease of clothes and garment space. Moire pattern and horizontal section map were useful to evaluate wearing conditions, and garment space was changed by the characteristics of the body shape.