• Journal of Environmental Impact Assessment
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• v.26 no.2
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• pp.160-170
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• 2017

The purpose of this study is to perform clustering of the habitat types and to identify the characteristics of species in the habitat types using mammal data (70,562) of the 3rd National Ecosystem Survey conducted from 2006 to 2012. The 15 habitat types recorded in the field-paper of the 3rd National ecosystem survey were reclassified, which was followed by the statistical analysis of mammal habitat types. In the habitat types cluster analysis, non-hierarchical cluster analysis (k-means cluster analysis), hierarchical cluster analysis, and non-metric multidimensional scaling method were applied to 14 habitat types recorded more than 30 times. A total of 7 Orders, 16 Families, and 39 Species of mammals were identified in the 3rd National Ecosystem Survey collected nationwide. When 11 clusters were classified by habitat types, the simple structure index was the highest (ssi = 0.07). As a result of the similarities and hierarchies between habitat types suggested by the hierarchical clustering analysis, the residential areas were the most different habitat types for mammals; the next following type was a cluster together with rivers and coasts. The results of the non-metric multidimensional scaling analysis demonstrated that both Mus musculus and Rattus norvegicus restrictively appeared in a residential area, which is the most discriminating habitat type. Lutra lutra restrictively appeared in coastal and river areas. In summary, according to our results, the mammalian habitat can be divided into the following four types: (1) the forest type (using forest as the main habitat and migration route); (2) the river type (using water as the main habitat); (3) the residence habitat (living near residential area); and (4) the lowland type (consuming grain or seeds as the main feeding resource).

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.10
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• pp.947-952
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• 2006

It has been confirmed that the inner defect of insulator and the perfect diagnosis for aging are closely related to safe electric power transmission system and that the detection of accident and diagnosis technique turn out to be very important issues. But perfect diagnosis is difficult because discharge pattern is irregular. Thus, we investigated discharge pattern using the new distribution statistical models with cross-inked polyethylene(XLPE) specimens. Voltage was applied to power frequency by step method, and calibration of discharge was set to 50 pC. After the voltage was applied, it measured the discharge occurring during 10s. We investigated discharge pattern using the K-means analysis and Weibull function. We also investigated variation of centroid and shape parameter due to variation of voltage. As a result of analyzing K-means, it was confirmed that cluster including many object numbers was formed by the presence of void. And result of Weibull distribution, it was confirmed that shape parameter of discharge varied from 1.28 to 1.62 in no void specimens, and that shape parameter of discharge number varied from 1.28 to 1.62. In the void, shape parameter of discharge varied from 5.66 to 6.43, and shape parameter of discharge number varied from 5.05 to 5.08.

• Journal of Fashion Business
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• v.16 no.1
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• pp.26-40
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• 2012

The purpose of this study is to examine the fashion lifestyle variable of global consumers and to compare among US, Chinese and EU consumers. The data were collected in US(n=749), China(n=702) and EU(n=1083) from 2008 to 2010. For analysis, factor analysis, reliability analysis, K-means cluster analysis and chi-square analysis of SPSS 18.0 are used. The research results are as follow: First, it is shown that significant lifestyle factors of global fashion consumers are "adventure seeking", "fashion-oriented", "conspicuous consumption", "leadership", "brand-oriented", and "DIY". As a result of the cluster analysis of lifestyle types, four cross-national market segments are identified. These segments can be labeled as follows: "conservative fashion-oriented group", "passive consumer group", "neutral consumer group", "active fashion-oriented group". Second, findings also reveal that fashion lifestyle segments had meaningful differences between nationality and by year. Third, the US consumers tended to have conservative fashion-oriented lifestyle in 2008, however global consumers were changed to have active fashion lifestyle in 2010. This research will be useful to global brands in planning marketing strategies by offering specific information for global consumer fashion lifestyle.

• Journal of Families and Better Life
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• v.26 no.3
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• pp.1-14
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• 2008

The purpose of the study was to examine the internet purchase behavior of items and self-assessment according to self-concept of elementary school students. The data were collected from 716 elementary school students by a self-administered questionnaire. Frequencies and means, Cronbach's Alpha, factor analysis, t-test, Pearson's correlation analysis, cross-tabulation analysis, cluster Analysis were conducted by SPSSWIN 12.0. The results from this study were as follows; First, from self-concept measurements, 4 factors(affective, social, schooling, Family self-concept) were extracted through factor analysis. Second, the subjects were classified into 3 clusters as self-concept types(high self-concept, middle self-concept, low self-concept) through cluster analysis. Third, the significant variables affecting internet purchase behaviors of items included grade, allowance, rank in class, the number of hours on the internet. As the self-concept gets higher, the frequence of the impulsive purchase and imitation purchases gets lower. In the contrary, as the self-concept gets higher, the self-assessment on the impulse purchases and imitation purchases also gets higher. In combination, these results suggest that irrational purchase behaviors were protected by positive self-concept, therefore it is important that children have positive a self-concept.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.367-374
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• 2010

As an alternative to the k-means clustering the k-spatial medians clustering has many good points because of advantages of spatial median. However, it has not been used a lot since it needs heavy computation. If the number of objects and the number of variables are large the computation time problem is getting serious. In this study we propose fast algorithm for the k-spatial medians clustering. Practical applicability of the algorithm is shown with some numerical studies.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.823-827
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• 2016

Determination of the colorectal cancer stage is possible only after surgery based on pathology results. However, sometimes this may prove impossible. The aim of the present study was to determine colorectal cancer stage using three clustering methods based on preoperative clinical findings. All patients referred to the Colorectal Research Center of Shiraz University of Medical Sciences for colorectal cancer surgery during 2006 to 2014 were enrolled in the study. Accordingly, 117 cases participated. Three clustering algorithms were utilized including k-means, hierarchical and fuzzy c-means clustering methods. External validity measures such as sensitivity, specificity and accuracy were used for evaluation of the methods. The results revealed maximum accuracy and sensitivity values for the hierarchical and a maximum specificity value for the fuzzy c-means clustering methods. Furthermore, according to the internal validity measures for the present data set, the optimal number of clusters was two (silhouette coefficient) and the fuzzy c-means algorithm was more appropriate than the k-means clustering approach by increasing the number of clusters.

• Spatial Information Research
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• v.20 no.6
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• pp.13-21
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• 2012

In this study, homogeneous regions for regional frequency analysis were identified using rainfall data from 61 observation points in Korea. The used data were gathered from 1980 to 2010. Self organizing map and K-means clustering based on Davies-Bouldin Index were used to make clusters showing similar rainfall patterns and to decide the optimum number of the homogeneous regions. The results from this analysis showed that the 61 observation points can be optimally grouped into 6 geographical clusters. Finally, the 61 observations points grouped into 6 clusters were mapped regionally using Thiessen polygon method.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.191-196
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• 1997

Fuzzy clustering has been playing an important role in solving many problems. Fuzzy c-Means(FCM) algorithm is most frequently used for fuzzy clustering. But some fixed point of FCM algorithm, know as Tucker's counter example, is not a reasonable solution. Moreover, FCM algorithm is impossible to perform the on-line learning since it is basically a batch learning scheme. This paper presents unsupervised learning networks as an attempt to improve shortcomings of the conventional clustering algorithm. This model integrates optimization function of FCM algorithm into unsupervised learning networks. The learning rule of the proposed scheme is a result of formal derivation based on the gradient descent procedure of a fuzzy objective function. Using the result of formal derivation, two algorithms of fuzzy cluster analysis, the batch learning version and on-line learning version, are devised. They are tested on several data sets and compared with FCM. The experimental results show that the proposed algorithms find out the reasonable solution on Tucker's counter example.

• The Research Journal of the Costume Culture
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• v.19 no.3
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• pp.622-636
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• 2011

The purpose of this study was to assess the effects of self-image congruence on attitudes toward purchase intentions of children's clothing via the Behavioral Intention Model. The empirical study was conducted via on-line survey and data were collected from mothers with children aged 6 to 10 years. A total of 593 respondents answered the questionnaire and 574 usable data were statistically analyzed. SPSS 18.0 was used to conduct descriptive statistical analysis, factor analysis, reliability analysis, cluster analysis, Chi-square test, ANOVA, and multiple regressions. A K-means cluster analysis was conducted based on three dimensions brand images of children's wear. Respondents were divided into four groups: elegant image group, multiple image group, ordinary image group, and childlike image group. Characteristics of consumers and clothing evaluative criteria that mothers considered important differed significantly across groups. Moreover, based on these groups, each dimension of self-congruence had different effects on brand attitude. Brand attitude and subjective norms had different effects on purchase intentions. In conclusion, levels of self-congruence and factors influencing purchase intention varied according to brand images of children's wear.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.7
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• pp.831-842
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• 2003

The purpose of this study is to analyze body types of Chinese women and provide concrete information on it by classifying them into several representative groups. As for the method of this study, statistical analysis is made of 79 items. This is done from July 18 to Aug 07, 2002, 525 female subjects from age 20 to 49 participated in this study. They all live in Beijing and Shanghai in China. The results of this study are as follows. 1. Means, standard deviations, the maximum and minimum of 19 items are extracted. The height and girth item have a high standard deviations. 2. 8 factors are extracted by using factor analysis. Factor 1: body obesity, Factor 2: vertical body size, Factor 3: upper body length Factor 4: size of ankles, Factor 5: angle of shoulders, Factor 6: length of hip Factor 7: size of shoulder, Factor 8: shape of chest 3. The body types of Chinese women are classified into 5 sub groups from the result of the Cluster analysis.