• Korean journal of food and cookery science
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• v.23 no.3 s.99
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• pp.327-336
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• 2007

Life-style factors often include social relationships as well as consumption, entertainment and dress patterns. They also typically reflect an individual's attitudes, values and worldview. Life-style types have become and an important factor for segmenting customer markets ever since significant relationships between life-style and customers' behavior was proven. This study examined the relationships between the life-styles of young customers' and the important attributes of foodservice encounters. Factors analysis with VARIMAX and K-means cluster analysis were conducted to group the subjects by life-style. According to the factors analysis, four underlying dimensions were identified and labeled: (1) 'actively fashioned', (2) 'luxury picky', (3) 'healthy toward', and (4) 'utilitarian leisure'. Based on the factor scores derived from the factors analysis, the K-means cluster analysis classified three groups as statistically significant using ANOVA(p<0.05). The overall mean score for the 3rd cluster 'trendy-active picky' was higher than the other two clusters, and represented very picky attitudes about foodservice attributes. The 3rd cluster also seemed to apply higher standards to all of the foodservice attributes. By order of importance, the most important attributes of the 2nd cluster 'pursue-utilitarian leisure' were food serving time, automation systems, server's hygienes, employee kindness, time in line, and menu variety. In spite of low concerns for the life-style attributes, the first cluster 'passively indifferent' recognized menu variety, food sanitation, food serving time, server's hygiene, menu price, air circulation, and room temperature as important. These results suggest that young diners in Korea could be classified by their diverse life-styles that are represented as trendy, utilitarian, and indifferent and will hopefully contribute to the foodservice industry's ability to segment customer characteristics by different life-styles in Korea.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.615-627
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• 2014

In this study, nonhierarchical K-mean cluster analysis is used to classify the types of 230 local governments and the Mann-Whitney U test and Kruskal-Wallis analysis are used to analyze the characteristics of pedestrian accidents by region types. With empirical analysis of pedestrian accidents, this study suggests improvements of walking environments reflecting local characteristics. Type 1-A (relatively dominant urban commercial areas), Type 1-B (predominantly urban residence) and Type 2 (rural areas) have been classified using nonhierarchical K-mean cluster analysis. According to the results, pedestrian accident rate on community roads was more than 60% for all types and incidence rate in rural areas was higher than that in urban areas. In addition, pedestrian accidents of Type 1-B have been found to occur more frequently than Type 2 in intersections and crossings, while the number of roadside casualties for Type 2 was highest.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.8
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• pp.1047-1052
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• 2004

The genomes of Chinese native chicken populations were screened using microsatellites as molecular markers. A total of, 528 individuals comprisede12 Chinese native chicken populations were typed for 7 microsatellite markers covering 5 linkage groups and genetic variations and genetic distances were also determined. In the 7 microsatellite loci, the number of alleles ranged from 2 to 7 per locus and the mean number of alleles was 4.6 per locus. By using fuzzy cluster, 12 Chinese native chicken populations were divided into three clusters. The first cluster comprised Taihe Silkies, Henan Game Chicken, Langshan Chicken, Dagu Chicken, Xiaoshan Chicken, Beijing Fatty Chicken and Luyuan Chicken. The second cluster included Chahua Chicken, Tibetan Chicken, Xianju Chicken and Baier Chicken. Gushi Chicken formed a separate cluster and demonstrated a long distance when comparing with other chicken populations.

• The Korean Journal of Franchise Management
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• v.7 no.1
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• pp.21-29
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• 2016

Purpose - The primary purpose of this study is to employ effective marketing methods using market segmentation of coffee shops by determining how motivations to visit coffee shops have different impacts on demographic profile of visitors and characteristics of coffee shop visits, so as to draw out a better understanding of customers of coffee market. Research design, data, and methodology - Data were collected using surveys of self-administered questionnaires toward coffee shop users in Daejeon, Korea. A number of samples used in data analysis were 253 excluding unusable responses. The data were analyzed through frequency, reliability, and factor analysis using SPSS 20.0. Factor analysis was conducted through the principal component analysis and varimax rotation method to derive factors of one or more eigen values. In addition, the cluster analysis, multivariate ANOVA, and cross-tab analysis were used for the market segmentation based on the types of motivation for coffee shop visits. The process of the cluster analysis is as follows. Four clusters were derived through hierarchical clustering, and k-means cluster analysis was then carried out using mean value of the four clusters as the initial seed value. Result - The factor analysis delineated four dimensions of motivation to visit coffee shops: ostentation motivation, hedonic motivation, esthetic motivation, utility motivation. The cluster analysis yielded four clusters: utility and esthetic seekers, hedonic seekers, utility seekers, ostentation seekers. In order to further specify the profile of four clusters, each cluster was cross tabulated with socio-demographics and characteristics of coffee shop visits. Four clusters are significantly different from each other by four types of motivations for coffee shop visits. Conclusions - This study has empirically examined the difference in demographic profile of visitors and characteristics of coffee shop visits by motivation to visit coffee shops. There are significant differences according to age, education background, marital status, occupation and monthly income. In addition, coffee shops use pattern characterization in frequency of visits to coffee shops, relationships with companion, purpose of visit, information sources, brand type, average expense per visit, important elements of selection attribute were significantly different depending on motivations for coffee shop visits.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.1
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• pp.59-67
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• 2015

Because reservoirs that supply irrigation water play an important role in water resource management, it is necessary to evaluate the vulnerability of this particular water supply resource. The purpose of this study is to provide water supply risk maps of agricultural reservoirs in South Korea using irrigation vulnerability model and cluster analysis. To quantify water supply risk, irrigation vulnerability indices are estimated to evaluate the performance of the water supply on the agricultural reservoir system using a probability theory and reliability analysis. First, the irrigation vulnerability probabilities of 1,346 reservoirs managed by Korea Rural Community Corporation (KRC) were analyzed using meteorological data on 54 meteorological stations over the past 30 years (1981-2010). Second, using the K-mean method of non-hierarchical cluster analysis and pre-simulation approach, cluster analysis was applied to classify into three groups for characterizing irrigation vulnerability in reservoirs. The morphology index, watershed area, irrigated area, and ratio between watershed and irrigated area are selected as the clustering analysis parameters. It is suggested that the water supply risk map be utilized as a basis for the establishment of risk management measures, and could provide effective information for a reasonable decision making on drought risk mitigation.

• Korean Journal of Community Nutrition
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• v.14 no.6
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• pp.722-733
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• 2009

The purpose of this study was to explore dietary patterns and compare dietary patterns using cluster and factor analysis in Korean adults. This study analyzed data of 4,182 adult populations who aged 30 and more and had all of socio-demographic, anthropometric, and dietary data from 2005 Korean Health and Nutrition Examination Survey. Socio-demographic data was assessed by questionnaire and dietary data from 24-hour recall method was used. For cluster analysis, the percent of energy intake from each food group was used and 4 patterns were identified: "traditional", "bread, fruit & vegetable, milk", "noodle & egg", and "meat, fish, alcohol". The "traditional" pattern group was more likely to be old, less educated, living in a rural area and had higher percentage of energy intake from carbohydrates than other pattern groups. "Meat, fish, alcohol" group was more likely to be male and higher percentage of energy intake from fat. For factor analysis, mean amount of each food group was used and also 4 patterns were identified; "traditional", "modified", "bread, fruit, milk", and "noodle, egg, mushroom". People who showed higher factor score of "traditional" pattern were more likely to be elderly, less educated, and living in a rural area and higher proportion of energy intake from carbohydrates. In conclusion, three dietary patterns defined by cluster and factor analysis separately were similar and all dietary patterns were affected by socio-demographic factors and nutrient profile.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.139-144
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• 2011

This study proposed a classification method of LIDAR data by using simultaneously the color information (R, G, B) and reflection intensity information (I) obtained from terrestrial LIDAR and by analyzing the association between these data through the use of statistical classification methods. To this end, first, the factors that maximize variance were calculated using the variables, R, G, B, and I, whereby the factor matrix between the principal factor and each variable was calculated. However, although the factor matrix shows basic data by reducing them, it is difficult to know clearly which variables become highly associated by which factors; therefore, Varimax method from orthogonal rotation was used to obtain the factor matrix and then the factor scores were calculated. And, by using a non-hierarchical clustering method, K-mean method, a cluster analysis was performed on the factor scores obtained via K-mean method as factor analysis, and afterwards the classification accuracy of the terrestrial LiDAR data was evaluated.

• Journal of Environmental Health Sciences
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• v.33 no.1 s.94
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• pp.49-56
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• 2007

The purpose of this study was to survey the distribution patterns of volatile organic compounds(VOCs) and formaldehyde in the various indoor environments using cluster analysis. We investigated VOCs and formaldehyde in subway stations, underground shopping areas, medical centers, maternity recuperation centers, public childcare centers, large stores, funeral houses, and indoor parking lots from June,2005 to May,2006. Concentration of TVOCs in maternity recuperations was 2,605.7 ${\mu}g/m^3$ that was higher than the guideline and other facilities. TVOCs in public childcare centers was 1,951.6 ${\mu}g/m^3$ also it exceeded the guideline. Moreover, concentration of TVOCs in every facility exceeded the guideline of Department of Environment, Korea. In case of formaldehyde, mean concentration, 336.5 ${\mu}g/m^3$, in only public childcare centers exceeded the 120 ${\mu}g/m^3$ of the guideline. Finally, by applying cluster analysis, three pattterns of the indoor air pollutions were distinguished. In the results of analysis, concentrations of TVOCs and formaldehyde of cluster 3 were higher than cluster 1 and 2 that were 2,561.4 ${\mu}g/m^3$ and 184.9 ${\mu}g/m^3$, respectively.

• Journal of Family Resource Management and Policy Review
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• v.14 no.3
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• pp.1-14
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• 2010

The purpose of this study was (1) to investigate the changes in consumer competencies according to the types of shopping value, (2) to reveal the effects of shopping value on consumer competencies. The subjects of this study were 266 university students dwelling in Seoul. A questionnaire was used as the survey method. The data was analyzed by Cronbach's alpha, frequencies, percentile, mean, factor analysis, K-mean cluster analysis, t-test, ANOVA and Duncan's multiple range tests, multiple linear regressions. Computations were conducted by SPSS WIN 12.0. The study produced the following results. First, college students can be categorized into 3 shopping values by K-means Cluster analysis of 13 items: the hedonic shopper (shopping value), the utilitarian shopper (shopping value) and the balanced shopper (shopping value). Second, there were significant differences in grades, satisfaction with life and shopping value. That is, grade 3and utilitarian shopping value group had a higher level of consumer competency. Third, the variable that influenced consumer competency was the utilitarian shopping value, influencing consumer attitude and consumer skill. These results imply that consumers should be constantly educated and that there needs to be a campaign to promote utilitarian shopping value.

• Korean Journal of Child Studies
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• v.31 no.4
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• pp.139-157
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• 2010

This study investigated the cognitive process of 5-year-old children, with a particular focus on gender, monthly age, and their tendencies towards hyperactivity through the performance of the Cognitive Assessment System (CAS; Das & Naglieri, 1997). The children with tendencies towards hyperactivity were identified based on Conners Teachers' Rating Scale (CTRS). The subjects were 75 five-year-old children in Seoul and surrounding metropolitan areas. Data were analyzed by means of descriptive statistics, an independent sample t-test, Pearson's correlation coefficient, one-way ANOVA, and by K-mean cluster analysis. Our results were as follows : (1) The CAS and CTRS' sub-factors were correlated negatively, except the positive correlation between planning factor and hyperactivity factor. (2) Girls exhibited significantly higher CAS scores in planning & sequential processing than boys. (3) The upper monthly age group (68-71 months) showed significantly higher score in terms of planning than the lower monthly age group (60-63 months). (4) The CAS scores of the children with tendencies towards hyperactivity was lower than that of normal children. (5) The CAS profile of 5-year-old children was divided into 4 groups with distinctive characteristics by means of K-mean cluster analysis.