• 한국의류산업학회지
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• 제10권5호
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• pp.618-624
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• 2008

The purposes of this study were to explore the perceived risk of smart clothing, to classify consumers by risk perception of smart clothing, and to investigate the differences among the segmented groups in regard to the evaluation of newness and innovativeness of smart clothing. In addition, the relationship among perceived risk, evaluation of newness and innovativeness of smart clothing were examined. A questionnaire was administered to 338 male and female subjects aged from 17 to 50. Analysis was performed by factor analysis, cluster analysis, ANOVA, and Pearson's correlation analysis. The results showed that the perceived risk of smart clothing was composed of 4 factors: economic risk, social risk, functional risk and physical risk. Consumers were classified into four groups: high risk perception group, low economic risk perception group, low functional risk perception group, and low social risk perception group. ANOVA showed that there were significant differences among four groups regard to the evaluation of newness and innovativeness of smart clothing. High risk perception group most highly evaluated the newness and innovativeness of smart clothing. There were positive correlation among the perceived risks, the evaluation of the newness and innovativeness of smart clothing.

• 한국의류산업학회지
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• 제13권6호
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• pp.827-837
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• 2011

The purpose of this study was to observe premium handbag users who are 20 to 30 year old of age, to determine purchase behavior and design preference of handbags by lifestyle and age variable to help market segmentation. A survey instrument was used. A sample was selected by quota sampling method from 20 to 30 aged Korean women, and reliable 538 data were analyzed by SPSS. Cluster type towards lifestyle and age were independent variable. There are two types of lifestyle clusters toward luxury handbags; Strongly favored and weakly favored group toward luxury handbags. Strongly favored group of luxury goods considered more brand, country of origin, and trend, while young people considered more new arrival of design in purchase of products. Preferred design type of hand bag was statistically associated with age variable. 20s consumers preferred shopper bag style and big size of handbags. Leather was preferred by the group of the strongly favored luxury goods. Through this research finding, we hope handbag brand market segmentation will be based on lifestyle and age variable to reflect customer's demand.

• 한국의류산업학회지
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• 제11권5호
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• pp.732-740
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• 2009

The purpose of this study was to classify consumers by orientation of clothing shopping and to analyze the type of preference store and recognized environmental elements of clothing shop. This survey was carried out through questionnaires of 312 females(Busan region) in their twenties and the data were analyzed by using Factor analysis, Cluster analysis, one-way ANOVA, Duncan test as a post identification and ${\chi}^2$-test. The results of this research can be summarizedas follows: First, orientations of clothing shopping consisted of pleasure shopping orientation, planned shopping orientation, brand shopping orientation, and information of store orientation. The respondents were classified into four groups by these characteristics: high interested shopping group, brand-oriented shopping group, low interested shopping group, and unplanned shopping group. These groups had different type of preference store and there were significantly different preferences among department store, brand agency and regular discount store. The recognized environmental elements of clothing shop were service policy, store atmosphere, store layout, trend of products, product variety, freedom of shopping, and reception service. The groups by characteristics of clothing shopping had different attitude regarding those factors. Especially, they are susceptible to store atmosphere, trend of products and reception service. There was the correlation between type of preference store and environmental elements of store and mainly recognized environmental elements of clothing shop had differences according to type of store.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.1886-1908
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• 2020

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them. Additionally, the presence of the imbalance data in big data is a massive concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework. In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm. The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm. The two-level query matching is performed for determining the requested data. The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data. The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21]. Here, the Parametric Enabled-Similarity Measure (PESM) is adapted for matching the similarities between two datasets. The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively.

• 수산해양기술연구
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• 제56권4호
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• pp.384-394
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• 2020

This paper proposes an outlier detection model based on machine learning that can diagnose the presence or absence of major engine parts through unsupervised learning analysis of main engine big data of a ship. Engine big data of the ship was collected for more than seven months, and expert knowledge and correlation analysis were performed to select features that are closely related to the operation of the main engine. For unsupervised learning analysis, ensemble model wherein many predictive models are strategically combined to increase the model performance, is used for anomaly detection. As a result, the proposed model successfully detected the anomalous engine status from the normal status. To validate our approach, clustering analysis was conducted to find out the different patterns of anomalies the anomalous point. By examining distribution of each cluster, we could successfully find the patterns of anomalies.

• 복식
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• 제65권7호
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• pp.1-18
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• 2015

The purpose of this paper is to compare the body types of Korean-Chinese and Han-Chinese women in their 60's dwelling in Yanbian Korean Autonomous Prefecture. In addition, a survey was conducted to analyze the social and environmental factors that would decide the body types of these two groups of individuals. 1) The comparison of 74 items in physical measurements between the Korean-chinese and Han-chinese showed differences in 36 items. 2) As for the body measurements of women, difference was found between the Han-Chinese and Korean-Chinese in all height items except bust height, circumference, depth, length, and weight. The values of Korean-Chinese were lower than those of Han-Chinese. 3) Cluster analysis shows that Korean- Chinese women appear thinner in regards to common weight, while Han-Chinese women appear to be thicker in regards to with common thinness. 4) Korean-Chinese women dwelling in Yanbian Korean Autonomous Prefecture maintained unique traditional customs and eating habits of Korea and were in constant contact with Korean cultures, which seems to make them care more about body management for health and beauty.

• International Journal of Costume and Fashion
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• 제15권2호
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• pp.19-36
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• 2015

The purpose of this study is to research the purchasing behavior according to the pursuit benefit for outdoor wear and to present a direction to outdoor wear. Questionnaire survey was administered to 533 male and female adults in their 30s to 40s living in Seoul and Gyeonggido from May 1 to 15, 2014. Concerning the statistic treatment for data analysis, SPSS for Window 18.0 was used to carry out frequency analysis, factor analysis, reliability test, cluster analysis, ANOVA and Duncan test as post-test. Benefit was drawn out as 4 elements including 'showing off & brand benefit sought', 'fashion benefits sought', 'functionality benefits sought' and 'economic benefits sought'. Group analysis according to benefit showed that it was materialized to 'multiple benefit sought group', 'unconcern group', 'showing off & brand benefit sought group' and 'utility benefit sought group'. There is an academic significance in that this research found out the level of benefit in purchasing outdoor wear and the difference of purchasing behavior by consumer groups according to benefit. This result might be used efficiently by marketers in outdoor clothing industry in classifying consumers and establishing the marketing strategy to deal with it.

• 한국의류산업학회지
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• 제13권4호
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• pp.487-498
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• 2011

The purpose of this study was to classify the lifestyles of 20's and 30's male consumers, identify the differences among the lifestyle groups, and analyze clothing values and buying behaviors according to the lifestyle types. The results of the study were as follows. First, as a result of cluster analysis to classify the lifestyles of 20's and 30's male consumers, 4 groups were identified as "adventurous and self-accomplishing type", "positive and socially active type", "active and trend seeking type", and "practical and information-oriented type". Second, 4 factors of Utilitarian Value were classified as "symbolism", "diversity of wearing purpose", "convenience of management", and "physical activeness". Third, 4 factors of Hedonic Value were extracted as "pursuit for confidence", "pursuit for sexual sensitivity", "pursuit for trend", and "pursuit for personality". Fourth, upon analyzing the differences of clothing values based on lifestyles, 4 groups considered the most important value as the pursuit of sexual sensibility. Fifth, the male consumers in 20's and 30's purchased to express individuality and made their own decision on buying the casual wear. Based on these results, adventurous and self-actualizing type and active and trend seeking type were 518 of 684 and represented male consumer in 20's and 30's.

• 한국의류산업학회지
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• 제11권3호
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• pp.445-452
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• 2009

This study aims to improve the fit of high school girls' uniforms. This study researched on the fit of the recent high school girls' uniforms and developed a sizing system that adequately reflects their body figures. The results are as follows. First, the research showed that over 50% of the highschool girls wore their blouses too tight, whereas over 60% of the girls wore skirts that were appropriately fitted. Second, to classify the high school girls' figures, factor analysis and cluster analysis were conducted. 5 factors were extracted for upper body and 3 factors for the lower body. The girls were then divided into 3 groups for the upper body and 4 groups for the lower body. Third, KS size intervals were used for frequency distribution of height and bust for the bodice, height and waist for the lower body. Sizing system of the uniform company and the frequency distribution of sizes were compared. As a result, sizing system of the uniform company and the frequency distribution slightly overlapped. Fourth, sizing system considering body type distribution and high frequency distribution section of sizes was suggested. As a result, for the upper body, when 6 sizes were suggested, 43.9% of the population was covered and when 11 sizes were suggested, 58.3% was covered. For the lower body, when 6 sizes were suggested, 32.2% was covered, and when 11 sizes were suggested 58.9% of the population was covered.

• 한국의상디자인학회지
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• 제21권4호
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• pp.165-179
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• 2019

This study used 3D body scan data to classify body shapes according to the torso shape of adult males aged 20-75 years. This data will be provided so that the apparel industry can make apparel products corresponding to body characteristics by age. The study used 1,796 adult males between the ages of 20 and 75 and the 3D body shape data of the '5th Research on National Standard Anthropometry'. For data analysis, the program SPSSWIN Ver. 17.0 was used to calculate the mean and frequency allowing for a factor analysis, cluster analysis, analysis of variance, and Duncan test. To classify body shape according to the torso shape of adult males, this study considered nine factors: 'horizontal size of torso,' 'vertical size of body,' 'curve of torso and waist-abdomen flatness ratio,' 'length of torso,' 'shape of neck area,' 'degree of lateral curve,' 'difference between front and back interscye length,' 'shoulder armscye shape,' and 'chest flatness ratio.' Based on the results of the factor analysis, the torso shapes of adult males were classified into five types. Type 1 is "upright body with flat, curvy shape", Type 2 is "curve sway back body type", Type 3 is "flat, abdominally obese body", Type 4 is "obese, crooked body" and Type 5 is "thick sway front body type." named.