• Korean journal of food and cookery science
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• v.19 no.3
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• pp.308-317
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• 2003

The purpose of this study was to identify combinations of factors, with regard to the use of restaurants by tourists, and to establish the relative importance of these factors in terms of their contribution to the total usage. Of 250 questionnaires, 209 were utilized for analysis in this study. Crosstabs, conjoint analysis, paired-samples t-test, k-means cluster analysis, one-way ANOVA analysis, and the Friedman test were used for the statistical analysis. The findings from this study were as follows: First, the Pearson's R and Kendall's tau statistics show that the model fits the data well. Second, it was found that 209 tourists most preferred restaurants that provided excellent quality traditional food, with a high quality of service, at a cheap price for the suburb. The 81 tourists of the first cluster most preferred restaurant that provided excellent quality fusion food, at a cheap price for the suburb. The 65 tourists of tile second cluster most preferred restaurant that provided average quality national food, at an expensive price for the suburb. The 63 tourists of the third cluster most preferred restaurant that provided excellent quality traditional food, at a reasonable price for the suburb. Third, it wis found that all tourists and the three clusters groups regarded both the type of food and its price to be very important factors. Finally, the results used in this study have provided some insight into the types of marketing strategies and tourism policies that may be successfully used by the operators and policymakers managing a location, the quality, price and type of food, and quality of service required by tourists dining at restaurants.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.5
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• pp.103-113
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• 2009

In this study, we separated the precipitation zones using the geographic location of stations and precipitation characteristics (monthly, seasonal, annual) in Gangwon province. Precipitation data of 66 weather stations (meterological office: 11 locations, auto weather system (AWS): 55 places) were used, and statistical method, K-means cluster method, was conducted for division of the precipitation regions. As the results of regional classification, the five zones of precipitation (Yongdong: 1 region, Youngseo: 4 regions) were separated. Seasonal average precipitation in spring is similar throughout Gangwon Province, seasonal average precipitation in summer has high values at Youngseo, and seasonal average precipitation in autumn and winter have high values at Youngdong. The some areas, the vicinity of Misiryeong and Daegwallyeong, happens the orographic precipitation in spatial analysis, but the orographic effects didn't occur for the whole Gangwon areas. However, to achieve more accurate results, the expansion of observatories per elevation and AWS data are demanded.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.397-412
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• 2023

In order for social robots to take root in human life, it is important to consider the technical implementation of social robots and human psychology toward social robots. This study aimed to derive potential social robot clusters based on the emotions consumers feel about social robot appearance design, and to identify and compare important design characteristics and emotional differences of each cluster. In our study, we established a social robot emotion framework to measure and evaluate the emotions consumers feel about social robots, and evaluated the emotions of social robot designs based on the semantic differential method, an kansei engineering approach. We classified 30 social robots into 4 clusters by conducting a multidimensional scaling method and K-means cluster analysis based on the emotion evaluation results, confirmed the characteristics of design elements for each cluster, and conducted a comparative analysis on consumer emotions. We proposed a strategic direction for successful social robot design and development from a human-centered perspective based on the design characteristics and emotional differences derived for each cluster.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.46-51
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• 2007

In this paper, we propose a new data clustering method using local probability and hypothesis theory. To cluster the test data set we analyze the local area of the test data set using local probability distribution and decide the candidate class of the data set using mean standard deviation and variance etc. To decide each class of the test data, statistical hypothesis theory is applied to the decided candidate class of the test data set. For evaluating, the proposed classification method is compared to the conventional fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm. The simulation results show more accuracy than results of fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.482-482
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• 2022

하천의 합류부에서는 수질이 다른 유체가 혼합하여 합류 전과 다른 특성을 보인다. 하천의 합류부에서 수질을 효율적으로 관리하기 위해서는 수질의 공간적인 혼합 특성을 규명하는 것이 중요하다. 합류부에서 수질의 공간적인 혼합 특성을 분석하기 위해 본 연구에서는 토폴로지 데이터 분석(topological data analysis, TDA), 자기 조직화 지도(Self-Organizing Map, SOM), k-평균 알고리즘(K-means clustering algorithm) 세 가지 기법을 이용하였다. 세 가지 기법을 비교하여 어떤 알고리즘이 합류부의 수질 변화 특성을 더 뚜렷하게 나타내는지 분석하였다. 수질 변화 비교 인자들은 pH, chlorophyll, DO, Turbidity 등이 있고, 수질 인자들은 YSI를 활용해 측정하였다. 자료의 측정 지역은 낙동강과 황강이 합류하는 지역이며, 보트에 YSI 장비를 부착하고 횡단하여 측정하였다. 측정한 데이터를 R 프로그램을 통해 세 가지 기법을 적용시켜 수질 변화 비교를 분석한다. 토폴로지 데이터 분석(topological data analysis, TDA)은 거대하고 복잡한 데이터로부터 유의미한 정보를 추출하는 데 사용하고, 자기조직화지도(Self-Organizing Map, SOM) 기법은 차원 축소와 군집화를 동시에 수행한다. k-평균 알고리즘(K-means clustering algorithm) 기법은 주어진 데이터를 k개의 클러스터로 묶는 머신러닝 비지도학습에 속하는 알고리즘이다. 세 가지 방법들의 주목적은 클러스터링이다. 클러스터 분석(Cluster analysis)이란 주어진 데이터들의 특성을 고려해 동일한 성격을 가진 여러 개의 그룹으로 대상을 분류하는 데이터 마이닝의 한 방법이다. 군집화 방법들인 TDA, SOM, K-means를 이용해 합류 지역의 수질 특성들을 클러스터링하여 수질 패턴들을 분석해 하천 수질 오염을 방지할 수 있을 것이다. 본 연구에서는 토폴로지 데이터 분석(topological data analysis, TDA), 자기조직화지도(Self-Organizing Map, SOM), k-평균 알고리즘(K-means clustering algorithm) 세 가지 기법을 이용하여 합류부에서의 수질 특성을 비교하며 어떤 기법이 합류의 특성을 더욱 뚜렷하게 나타내는지 규명했다. 합류의 특성을 군집화 방법을 이용해 알게 된다면, 합류부의 수질 변화 패턴을 다른 합류 지역에서도 적용할 수 있을 것으로 기대된다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.1005-1013
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• 2021

With the prolonged COVID-19, the existing academic gap is widening. The purpose of this study is to provide homeroom teachers with a visual confirmation of the academic achievement gap in grades and classrooms through academic achievement analysis, and to use this to help them design lessons and explore ways to improve the academic achievement gap. The data of students' Korean and math diagnostic evaluation scores at the beginning of the school year were visualized as clusters using the K-means algorithm, and as a result, it was confirmed that a meaningful clusters were formed. In addition, through the results of the teacher interview, it was confirmed that this system was meaningful in improving the academic achievement gap, such as checking the learning level and academic achievement of students, and designing classes such as individual supplementary instruction and level-specific learning. This means that this academic achievement data analysis system helps to improve the academic gap. This study provides practical help to homeroom teachers in exploring ways to improve the academic gap in grades and classes, and is expected to ultimately contribute to improving the academic gap.

• Korean journal of food and cookery science
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• v.19 no.5
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• pp.581-590
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• 2003

The purpose of this study was to identify the combinations of factors combinations conferring the highest utility of family restaurants to university students, and establish the relative importance of these factors in terms of their contribution to total utility. 196 of 200 questionnaires were utilized for the analysis. (Eds note: to whom were the questionnaires administered) Frequencies, crosstabs, and the conjoint, max. utility, BTL and Logit models, K-means cluster and one-way ANOVA analysesis, and the Friedman test were the statistical methods used for this study. The findings from this study were as follows: 1) the Pearson's R and Kendall's tau statistics (Eds note: these were not mentioned earlier) show that the model (Eds note: which model is this) fits the data well. 2) it was found that of all the respondents, especially the first and third clusters, regarded both the type of food and the price as very important factors. 3) it was found that all the respondents, especially the third cluster, most preferred a family restaurant (design and simulation) that provided less than 6 fusion and traditional foods. The first cluster most preferred family restaurant (design) that provided over 10 traditional and less than 6 ethnic foods. The second cluster most preferred a family restaurant (design and simulation) that provided over 10 traditional foods. 4) the results of the study have provided some insights into the effective types of family restaurant designs that can be successfully developed by those who manage menu variety, quality and type of food, price, and quality of service to university students dining at family restaurants.

• Journal of Korean Society of Rural Planning
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• v.21 no.4
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• pp.35-43
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• 2015

The purpose of the research is to analyze damages of TYLCV (Tomato Yellow Leaf Curl Virus) in the context of climate changes and to find the spatial distribution of the damages and characteristics of regions. A TYLCV is generally known for a plant disease related to temperature. Its occurrence rate increases when temperature rises. This disease first occurred in 2008 and rapidly spread nationwide. Due to the spread of a TYLCV, a number of Tomato farms in Korea were damaged severely. To analyze damages of the pest in the context of climate changes, this research estimated production loss under the current situation and RCP scenarios. Additionally, Hot Spot Analysis, LISA, and Cluster analysis were conducted to find spatial distribution and properties of largely damaged regions under RCP scenarios. The results explained that additional production loss was estimated differently by regions with the same temperature rising scenario. Also, largely damaged regions are spatially clustered and factors causing large damages were different across regional cluster groups. It means that certain regions can be damaged more than others by diseases and pests. Furthermore, pest management policy should reflect the properties of each region such as climate conditions, cultivate environment and production technologies. The findings from this research can be utilized for developing rural management plans and pest protection policies.

• The Research Journal of the Costume Culture
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• v.22 no.6
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• pp.899-910
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• 2014

The purpose of this study was to analyze high school students' school uniform wearing attitude by group according to clothing attitude targeting Gyeongnam area. This study aims to provide preliminary data in the field of school uniform and marketing that clothing propensity by groups is considered. This study conducted a survey targeting 762 high school students in Gyeongnam. For statistical analysis, SPSS for Window 14.0 was used for frequency analysis, factor analysis, reliability analysis, multiple sponse analysis, cluster analysis, ANOVA analysis and Duncan's ex-post analysis method. As a result of cluster analysis on the clothing attitude, students were divided into 4 segmentation of fashion seeking group, fashion indifference group, conformity group and modesty group. As a result of verification on the difference in perception toward wearing school uniform by groups which were classified according to the propensity of clothing attitude, activity, stability, and practicality were all varied according to the propensity of clothing attitude. 4 groups were significant differences in the degree of consent to wearing school uniform, price of school uniforms, tendency to prefer famous brand when purchasing school uniform, experience of transforming school uniform, opinion about school uniform modification and reason for school uniform modification. While low graders were many in 'modesty group', upper graders were many in 'fashion seeking group', which means that more segmentalized satisfaction of clothing by group may be raised if such a fact is considered when planning clothing for high school students segmentalized by age.