• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.35.1-35.1
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• 2016

To understand the coevolution of Brightest Cluster Galaxies (BCGs) and their host clusters, we conduct a case study on the BCGs in dynamically young and old clusters, Abell 1139 (A1139) and Abell 2589 (A2589). We analyze the pixel color-magnitude diagrams (pCMDs) using deep g- and r-band images, obtained from the CFHT observations. (1) While the overall shapes of the pCMDs are similar to those of typical early-type galaxies, the A2589-BCG tends to have redder mean pixel color and smaller pixel color deviation at given surface brightness than the A1139-BCG. (2) The mean pixel color distribution as a function of pixel surface brightness indicates that the A2589-BCG formed a larger central body by major dry mergers at an early epoch than the A1139-BCG, while they have grown commonly by subsequent minor mergers. (3) The spatial distributions of the pixels with deviated colors reveal that the A1139-BCG experienced considerable tidal events more recently than the A2589-BCG, whereas the A2589-BCG has an asymmetric compact core possibly resulting from major dry merger at an early epoch. (4) The A2589-BCG shows a very large faint-to-bright pixel number ratio compared to early-type non-BCGs, whereas the ratio for the A1139-BCG is not distinctively large. These results imply that the BCG in the dynamically older cluster (A2589) formed earlier and is relaxed better.

• International Journal of Oral Biology
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• v.43 no.2
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• pp.101-111
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• 2018

Understanding the classification of malocclusion is a crucial issue in Orthodontics. It can also help us to diagnose, treat, and understand malocclusion to establish a standard for definite class of patients. Principal component analysis (PCA) and k-means algorithms have been emerging as data analytic methods for cephalometric measurements, due to their intuitive concepts and application potentials. This study analyzed the macro- and meso-scale classification structure and feature basis vectors of 1020 (415 male, 605 female; mean age, 25 years) orthodontic patients using statistical preprocessing, PCA, random matrix theory (RMT) and k-means algorithms. RMT results show that 7 principal components (PCs) are significant standard in the extraction of features. Using k-means algorithms, 3 and 6 clusters were identified and the axes of PC1~3 were determined to be significant for patient classification. Macro-scale classification denotes skeletal Class I, II, III and PC1 means anteroposterior discrepancy of the maxilla and mandible and mandibular position. PC2 and PC3 means vertical pattern and maxillary position respectively; they played significant roles in the meso-scale classification. In conclusion, the typical patient profile (TPP) of each class showed that the data-based classification corresponds with the clinical classification of orthodontic patients. This data-based study can provide insight into the development of new diagnostic classifications.

• Journal of Family Resource Management and Policy Review
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• v.13 no.2
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• pp.129-151
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• 2009

The first purpose of this study was to reveal the types of shopping value of college students. The second purpose was to examine the change in the perception and management behaviors related to credit cards according to the types of shopping value. The third purpose was to examine the effects of shopping value on perception and management behaviors on credit cards. The data were collected from 392 college students in Seoul by a self-administered questionnaire. Analyses including frequency, mean, factor analysis, Cronbach's alpha, Pearson's correlation analysis, Crosstabulation analysis, analysis of variance, K-means Cluster analysis and Multiple linear regression were conducted using SPSS WIN12.0. The major findings were as follows. First, college students can be categorized into 3 types of shopping values by K-means Cluster analysis of 14 items. The groups were entitled the hedonistic shopping value, the utilitarian shopping value, and the saving shopping value. Second, positive perception and management behaviors related to credit cards were different depending on the types of shopping value. The hedonistic shopping value group had a higher level of positive perception of credit cards and a lower level of credit card management, compared with the other groups. The saving shopping value group had higher levels of both positive perception and management of credit cards. Among the three groups, the utilitarian shopping group had the lowest level of positive perception of credit cards, despite having ahigher level of credit card management. Lastly, the most effective variance on credit card management was the utilitarian shopping value. These results suggest that a healthy shopping value is very important for having a healthy perception and management of credit cards, because shopping value is a critical variance to affect perception and management of credit cards.

• Journal of the Korean Society of Clothing and Textiles
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• v.17 no.2
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• pp.247-264
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• 1993

This study deals with the manufacturing of the foundation garments for the well-balanced somatotype of the Korean middle-aged womenhood. In order to get hold of the different somatotypes, a survey of a total of 134 middle-aged women in Kwangju area, ranging in their age from 45 through 59 was made. The statistical methods used for the analysis of the basic data were the Pearson's correlation coefficient, Anova, Cluster analysis and Stepwise. Emphasis of the try-on test was placed on (1) the comparison of anthropometric data before and after trying on the foundation garments, (2) sensory evaluation, (3) a rating on fit and performance, (4) the comparison by means of photograph. The conclusions obtained are as follows : 1) The 134 women sampled and measured were classified into the five groups of somatotype : the 52 women (34%) belong to Cluster 1 ; the 22 women(14.5%) belong in Cluster 2 ; the 12 women(7.9%) belong in Cluster 3 ; the 15 women(9.9%) belong in Cluster 4 ; the 33 women(27.7%) belong to Cluster 5. 2) As for the characteristics of the foundation garment design, the V-shaped neckline and chest dart was used. The adjust point is right above the perineum point. The foundation garment length is as far as trochanteric point. The materials used are cotton/polyurethane, lace, 100%cotton. The materials used for corrections were the sponge pad for the chest, and non-woven fabric pad for the back, shoulder and the hip. 3) The comparison of the anthropometric data of the subject when dressed in foundation garments showed a significant difference in bust point height, in bust point length and in nipple-ta-nipple breadth, which proves the foundation garments to be effective in correcting such part as the chest, the hip and the abdomen. 4) As considered in terms of the sensory evaluation, the item except for the shoulder and the armhole coincided with each other in the mean value and in the composite reliability coefficient, which also proves the foundation garments to be effective. 5) Subjects were satisfactory on fit, performance, design, of the foundation garment, and their changed appearance. 6) In the case of the comparison through the photographs, the silhouettes of all the five women subjects were found effectively to be balanced.

• The Korean Journal of Mycology
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• v.42 no.2
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• pp.159-164
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• 2014

For development of a method for differentiation of Pleurotus eryngii cultivars, simple sequence repeats (SSR) from whole genomic DNA sequence analysis was used for genotyping and two multiplex-SSR primer sets were developed. These SSR primer sets were employed to distinguish 12 cultivars and strains. Five polymorphic markers were selected based on the genotyping results. PCR using each primer produced one to four distinct bands ranging in size from 200 to 300 bp. Polymorphism information content (PIC) values of the five markers were in the range of 0.6627 to 0.6848 with an average of 0.6775. Unweighted pairgroup method with arithmetic mean clustering analysis based on genetic distances using five SSR markers classified 12 cultivars into two clusters. Cluster I and II were comprised of four and eight cultivars, respectively. Two multiplex sets, Multi-1 (SSR312 and SSR366) and Multi-2 (SSR178 and SSR277) completely discriminated 12 cultivars and strains with 21 alleles and a PIC value of 0.9090. These results might be useful in providing an efficient method for the identification of P. eryngii cultivars with separate PCR reactions.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1131-1131
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• 2001

To develop a nondestructive quality evaluation technique of fruits, a K-mean algorism is applied to near infrared (NIR) spectroscopy of apples. The K-mean algorism is one of neural network partition methods and the goal is to partition the set of objects O into K disjoint clusters, where K is assumed to be known a priori. The algorism introduced by Macqueen draws an initial partition of the objects at random. It then computes the cluster centroids, assigns objects to the closest of them and iterates until a local minimum is obtained. The advantage of using neural network is that the spectra at the wavelengths having absorptions against chemical bonds including C-H and O-H types can be selected directly as input data. In conventional multiple regression approaches, the first wavelength is selected manually around the absorbance wavelengths as showing a high correlation coefficient between the NIR $2^{nd}$ derivative spectrum and Brix value with a single regression. After that, the second and following wavelengths are selected statistically as the calibration equation shows a high correlation. Therefore, the second and following wavelengths are selected not in a NIR spectroscopic way but in a statistical way. In this research, the spectra at the six wavelengths including 900, 904, 914, 990, 1000 and 1016nm are selected as input data for K-mean analysis. 904nm is selected because the wavelength shows the highest correlation coefficients and is regarded as the absorbance wavelength. The others are selected because they show relatively high correlation coefficients and are revealed as the absorbance wavelengths against the chemical structures by B. G. Osborne. The experiment was performed with two phases. In first phase, a reflectance was acquired using fiber optics. The reflectance was calculated by comparing near infrared energy reflected from a Teflon sphere as a standard reference, and the $2^{nd}$ derivative spectra were used for K-mean analysis. Samples are intact 67 apples which are called Fuji and cultivated in Aomori prefecture in Japan. In second phase, the Brix values were measured with a commercially available refractometer in order to estimate the result of K-mean approach. The result shows a partition of the spectral data sets of 67 samples into eight clusters, and the apples are classified into samples having high Brix value and low Brix value. Consequently, the K-mean analysis realized the classification of apples on the basis of the Brix values.

• The Plant Pathology Journal
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• v.19 no.5
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• pp.221-226
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• 2003

Twenty universal rice primers (URPs) were used to detect PCR polymorphisms in 25 isolates of six different Alternaria species producing host specific toxins (HST). Eight URPs could be used to reveal PCR polymorphisms of Alternaria isolates at the intra- and inter-species levels. Specific URP-PCR polymorphic bands that are different from those of the other Alternaria spp. were observed on A. gaisen and A. longipes isolates. Unweighted pair-group method with arithmetic mean (UPGMA) cluster analysis using 94 URP polymorphic bands revealed three clustered groups (A. gaisen group, A. mati complex group, and A. logipes group).

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.4
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• pp.197-212
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• 2019

The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.41-53
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• 2015

In meteorological field, many researchers have tried to develop micro scale weather analysis modules for providing real-time weather information service in the metropolitan area. This effort enables us to cope with various economic and social harms coming from serious change in the micro meteorology of a metropolitan area due to rapid urbanization such as quantitative expansions in its urban activity, growth of population, and building concentration. The accuracy of the micro scale weather analysis modules (MSWAM) directly related to usefulness and quality of the real-time weather information service in the metropolitan area. This paper design a evaluation system along with verification tools that sufficiently accommodate spatio-temporal characteristics of the outputs of the MSWAM. For this we proposes a test for the equality of mean vectors of the output series of the MSWAM and corresponding observed time series by using a spectral analysis technique. As a byproduct, a time series cluster analysis method, using a function of the test statistic as the distance measure, is developed. A real data application is given to demonstrate the utility of the method.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.2
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• pp.93-101
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• 2015

This study was conducted to analyze the relationship between tree-ring growth of Quercus acutissima and climatic variables by dendroclimatological method. Annual tree-ring growth data of Quercus acutissima collected by the $5^{th}$ National Forest Inventory (NFI5) were organized to analyze the spatial distribution of the species growth pattern. To explain the relationship between tree-ring growth of Quercus acutissima and climatic variables, monthly temperature and precipitation data from 1950 to 2010 were compared with tree-ring growth data for each county. When tree-ring growth data were analyzed through cluster analysis based on similarity of climatic conditions, four clusters were identified. In addition, index chronology of Quercus acutissima for each cluster was produced through cross-dating and standardization procedures. The adequacy of index chronologies was tested using basic statistics such as mean sensitivity, auto correlation, signal to noise ratio, and expressed population signal of annual tree-ring growth. Response function analysis was conducted to reveal the relationship between tree-ring growth and climatic variables for each cluster. The results of this study are expected to provide valuable information necessary for estimating local growth characteristics of Quercus acutissima and for predicting changes in tree growth patterns caused by climate change.