• The Korean Journal of Community Living Science
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• v.15 no.4
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• pp.67-78
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• 2004

This study was designed to understand purchase motivation for clothing, evaluation criteria for clothing, evaluation criteria for stores depending on average monthly expense for clothing among female college students in Yanbian, China. Questionnaire was used for measurement tools to study the subject of the thesis. Each question was rated in 5 point scale, where 1 means 'not at all' and 5 means 'definitely'. The main study was conducted against 300 female college students from May 17 to June 5, 2001. The data for the study were analyzed using SAS PC program for frequency distribution, percentage, t -test. The purchase motivation for clothing, evaluation criteria for clothing, evaluation criteria for stores are affected by average monthly expense for clothing. The result was showed as follows : A significant difference was showed in the purchase motivation, evaluation criteria for clothing, evaluation criteria for stores, preferred stores between lower group and higher group depending on monthly expense on clothing. A meaningful difference was showed in 4 areas'to try a new trend, impulsive buying at the store display, for a change of mood, for stylish appearance'in the purchase motivations for clothing depending on the monthly expense for clothing, in 2 areas'brand, trendy fashion'in evaluation criteria for clothing depending on the monthly expense for clothing, in 3 areas 'after service, hospitality of sales person, brand awareness' in the evaluation criteria for stores depending on the monthly expense for clothing, And Respondents of a high expense group for clothing showed higher means compared to a low expense group for clothing in all areas. A significant difference was showed among groups regarding preferred stores based on the monthly expense on clothing.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.671-672
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• 2008

K-means is a popular one in clustering algorithms, and it minimizes the mutual euclidean distance among the sample points. But K-means has some demerits, such as depending on initial condition, unsupervised learning and local optimum. However mahalanobis distancecan deal this case well. In this paper, the author proposed a new clustering algorithm, named exponential probability clustering, which applied Mahalanobis distance into K-means clustering. This new clustering does possess not only the probability interpretation, but also clustering merits. Finally, the simulation results also demonstrate its good performance compared to K-means algorithm.

• The Research Journal of the Costume Culture
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• v.8 no.1
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• pp.65-76
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• 2000

The purposes of this study were to claassify of clothing-wearing motives, to analyze tendency of fashion in campus, and to propose fashion merchandising plans and marketing strategies of companies for female college students. Research had been done during 1 week each on April and October in 1998 and 1999. Sixty-two students in spring semester and 54 student in fall semester were surveyed and analyzed. Their grades and majors were not considered in this study. Fashion Diary had been written by students and then their statements were analyzed by the means of the qualitative data analysis. Following conclusions were obtained from this study. 1. Clothing-wearing motives were classified with 'group depending on daily work', 'group depending on image', 'group depending on body supplement', 'group depending on weather', 'group of fashion following', 'group depending on courtesy', 'group depending on mood', and 'group of unconcerning'. 2. The fashion tendency of the students were layered look and they preferred casual styles, such as round t-shirts, jean pants, and cardigans. The color of white, black, and gray were prevalent for top wear, and the color of blue, black, beige for bottom wear in both seasons. For shoes, color of black, brown, white were popular. For bags, black, blue, and khaki color were prevailed. 3. The proposals obtained from this study are as follows : 1) Production of clothing enforced functional and practical base is wanted for 'group depending on daily work'. 2) Designs considering body parts such as hip, arm hole, shoulder, and thigh are needed. 3) Utilization of new materials such as waterproof with ventilation, opaque (means 'not see-through'), antipollution finishing process are required. 4) Simple designs of clothing which could be performed by herself with various accessories. Thermochromic materials are wanted for 'group of fashion following'. 5) Diversity of semi-suit is required for 'group depending on courtesy'.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.867-874
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• 2004

K-Means algorithm is a non-hierarchical (plat) and reassignment techniques and iterates algorithm steps on the basis of K cluster centroids until the clustering results converge into K clusters. In its nature, K-Means algorithm has characteristics which make different results depending on the initial and new centroids. In this paper, we propose the modified K-Means algorithm which improves the initial and new centroids decision methodologies. By evaluating the performance of two algorithms using the 16 weighting scheme of SMART system, the modified algorithm showed $20{\%}$ better results on recall and F-measure than those of K-Means algorithm, and the document clustering results are quite improved.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.55-67
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• 2015

An important problem in cluster analysis is the selection of variables that define cluster structure that also eliminate noisy variables that mask cluster structure; in addition, outlier detection is a fundamental task for cluster analysis. Here we provide an automated K-means clustering process combined with variable selection and outlier identification. The Automated K-means clustering procedure consists of three processes: (i) automatically calculating the cluster number and initial cluster center whenever a new variable is added, (ii) identifying outliers for each cluster depending on used variables, (iii) selecting variables defining cluster structure in a forward manner. To select variables, we applied VS-KM (variable-selection heuristic for K-means clustering) procedure (Brusco and Cradit, 2001). To identify outliers, we used a hybrid approach combining a clustering based approach and distance based approach. Simulation results indicate that the proposed automated K-means clustering procedure is effective to select variables and identify outliers. The implemented R program can be obtained at http://www.knou.ac.kr/~sskim/SVOKmeans.r.

• The Korean Journal of Community Living Science
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• v.16 no.4
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• pp.59-69
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• 2005

This study examined the shop selection standards and preferred shops depending on the clothing attitudes identified by the psychological characteristics of consumers. To this end, this study selected the Korean Chinese college women in Yanbian. The study was conducted against 300 college students from May to June, 2002. Questionnaire was used for studying the subject of the thesis. Each question was rate4 in 5 point scale, where 1 means 'not at all' and 5 means 'definitely'. The data of this study was statistically analyzed using the SAS PC program. The t-test and $X^{2}$ were conducted to identify the evaluation criteria for clothing store and the preferred shops depending on clothing attitude groups and the factor analysis was carried out to analyze the clothing behavior factors. The results of study are summarized as described below. The clothing attitude of college women was classified into four factors: fashionable, brand-oriented, aesthetic and modest. The subjects were divided into two groups with higher average score and that with lower average score by factor, respectively. As a result of study on the evaluation standards of shop selection and preferred shops depending on the clothing attitude, for the evaluation standards of shop selection, three factors, fashionable, brand-oriented and modest factors, showed the significant difference between two groups. There was a significant difference between two groups in fashionable and brand-oriented factor and the preferred shops.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.75-84
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• 2021

For analyzing big data, the social network is increasingly being utilized through relational data, which means the connection characteristics between entities such as people and objects. When the relational data does not exist directly, a social network can be configured by calculating relational data such as attribute similarity from attribute data of entities and using it as links. In this paper, the composition method of the social network using the attribute similarity between entities as a connection relationship, and the clustering method using subgroups for the configured social network are suggested, and the clustering effectiveness of the clustering results is evaluated. The analysis results can vary depending on the type and characteristics of the data to be analyzed, the type of attribute similarity selected, and the criterion value. In addition, the clustering effectiveness may not be consistent depending on the its evaluation method. Therefore, selections and experiments are necessary for better analysis results. Since the analysis results may be different depending on the type and characteristics of the analysis target, options for clustering, etc., there is a limitation. In addition, for performance evaluation of clustering, a study is needed to compare the method of this paper with the conventional method such as k-means.

• Journal of Information Processing Systems
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• v.4 no.2
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• pp.67-76
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• 2008

This research proposes a new strategy where documents are encoded into string vectors and modified version of k means algorithm to be adaptable to string vectors for text clustering. Traditionally, when k means algorithm is used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text clustering, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the k means algorithm adaptable to string vectors for text clustering.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.497-508
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• 2005

This paper aims at studying on K-means Clustering focusing on initialization which affect the clustering results in K-means cluster analysis. The four different methods(the MA method, the KA method, the Max-Min method and the Space Partition method) were compared and the clustering result shows that there were some differences among these methods, especially that the MA method sometimes leads to incorrect clustering due to the inappropriate initialization depending on the types of data and the Max-Min method is shown to be more effective than other methods especially when the data size is large.

• Korean Institute of Interior Design Journal
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• v.24 no.1
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• pp.104-114
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• 2015

The library means to children a place where they find information necessary for them among a variety of knowledge and information to address their intellectual curiosity, which makes their lives wise and mature. There are not enough research materials understanding children's cognitive systems and behaviors in studies of a children's library with this necessity. This study divided and analyzed the behaviors of children, who were users into three types of reading in terms of information processing. First, it observed the types of reading depending on the stage of their cognitive development; Second, it analyzed the patterns of their behaviors through plays; and lastly, it presented the phases of their behaviors depending on the degree of an assistant's intervention. As a result of the study, the sensory system used and the space preferred differed depending on the stage of their cognition; there were differences in the patterns of their behaviors in each age group depending on the type of play; and there were differences in the children's ability of information processing depending on the assistant's ability. It is anticipated that there will be improvements of children's reading ability at the reading room in a better environment if the three types are considered in terms of information processing.