• Journal of Global Scholars of Marketing Science
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• v.16 no.1
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• pp.73-91
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• 2006

This study was conducted for market segmentation of cruise tourist according to launching the "T" in Busan. Benefit segmentation was used to identify attributes of cruise services; importance of ship's physical appearance and importance of service and activities. 24 attributes were distilled to 5 factors: 'Facility & Service', 'Atmospherics of cruise ship', 'Escape', 'Choice', 'Safety'. A K-means cluster analysis identified three clustered segments for five importance factors in which high loyalty customers were found to be the most important segment. Based on the findings, three distinct groups were formed: 'Moderators', 'High Loyalty', 'Spurious'. The most important factors by high loyalty groups were identified 'Safety', 'Facility & Service', 'Atmospherics of cruise ship', 'Choice', 'Escape' in order. The results of the study showed statistically significant differences among the three groups in terms of demographic and behavioral variables. Especially, the target market should be considered by 'High Loyalty' group and 'Moderators' group in order. Positioning strategies and marketing mix strategies for effectively targeting the segments were discussed.

• Korean Journal of Environmental Biology
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• v.23 no.3 s.59
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• pp.238-249
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• 2005

The present study was carried out to apprehend that the pollutants originating from Jinhae Industrial Complex affect benthic polychaetous community in the northern Jinhae bay. An investigation on the macrobenthic community in Jinhae bay was conducted in September, December of 2002 and March of 2003. The benthic fauna showed mean density of 2,151 ind. $m^{-2}$ in September of 2002, 2,427 ind. $m^{-2}$ in December of 2002 and 2,394 ind. $m^{-2}$ in March of 2003. Major faunal groups are polychaetes, crustaceans and mollusks, corresponding to $73.7\%,\;12.0\%\;and\;11.7\%,$ in total mean density during all of the sampling season, respectively. The most abundant species was Lumbrineris longifolia $(24.85\%),$ followed by Tharyx sp. $(21.70\%),$ Mesochaetopterus sp. $(6.20\%),$ Heteromastus filiformis $(5.39\%),$ Prionospio sp. $(5.18\%),$ Clycinde sp. $(4.29\%),$ etc. Tharyx sp. was the highest abundant species in September of 2002, and Lumbrineris longifolia was the dominant species in another sampling seasons. The density and the species number of polychaetes were high around Chori Is. and poor near Jinhae Industrial Complex area. Cluster analysis based on the species composition showed that Jinhae bay could be divided into three regions except in March of 2003. In December of 2002, there are very distinct regions by the cluster analysis. The density of benthic polychaetes in Jinhae bay was higher than that in the other coastal area of Korea, due to the predominance by some of opportunistic species, such as Lumbrineris longifolia, Tharyx sp. and Heteromastus filiformis, etc. It means that the study area were in the process of organic enrichment.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.7 s.144
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• pp.959-967
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• 2005

The color of apparels has the interaction of the face skin colors of the wearers. This study was carried out to classify the face skin colors of Korean males into several similar face skin colors in order to extract favorable colors which flatter to their face skin colors. The criterion that select the new subjects who have the classified face skin colors have to be decided. With color spectrometer, JX-777, face skin colors of subjects were measured quantitatively and classified into three clusters that had similar hue, value and chroma with Munsell Color System. Sample size was 418 Korean males and other 15 of new males subjects. Data were analyzed by K-means cluster analysis, ANOVA, Duncan multiple range test, Stepwise discriminant analysis using SPSS Win. 12. Findings were as follows: 1. 418 subjects who have YR colors were clustered into 3 kinds of face skin color groups. 2. Discriminant variables of face skin colors was 4 variables : L value of forehead, v value of cheek, c value of forehead, and b value of cheek from standardized canonical discriminant function coefficient 1 and c value of forehead, L value of forehead, b value of cheek. and L value of cheek from standardized canonical discriminant function coefficient 2. 3. Hit ratio of type 1 was $92.3\%$, of type 2 was $96.5\%$ and of type 3 was $92.6\%$ by the canonical discriminant function of 4 variables. 4. The canonical discriminant function equation 1 and 2 were calculated with the unstandardized canonical discriminant function coefficient and constant, the cutting score, and range of the score were computed. 5. The criterion that select the new subjects who have the classified face skin colors was decided.

• Journal of the Korean Home Economics Association
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• v.46 no.1
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• pp.63-72
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• 2008

The purpose of this study was to investigate the appearance management behaviors and motives differentiated by the body image of college men. A questionnaire was designed for the survey and the subjects were 228 college in Pusan and the Kyongnam province, Korea. The statistical analyses were carried out in the forms of frequency, factor analysis, cluster analysis, ANOVA and Duncan's multiple range test. The findings throughout the research are as follows; First, according to the multidimensional body image of the subjects, the college men were classified into three groups, i.e. the appearance concerning and satisfied group, the weight concerning group, and the appearance indifferent group. The appearance management behaviors of the college men consist of innovative appearance management, weight reduction, appearance management through apparel and fashion products, body shape care, skin care, hair care and health care. Second, the weight concerning group showed a higher BMI than those of the other groups. The means of the monthly income and the expenses for the appearance management of the appearance indifferent group were lower than those of the other groups. Third, the college men were generally shown to pursue the motive improving sociality. The appearance concerning and satisfied group and the weight concerning group indicated higher pursuing motives than the appearance indifferent group in the appearance management motives. Finally, there were significant differences in the appearance management behaviors among the three groups. While the weight concerning group showed the more concerning appearance management behaviors, the appearance indifferent group had the least appearance concerning tendency.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.23-43
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• 2022

In order to apply AI (Artificial Intelligence) in various industries, interest in algorithm selection is increasing. Algorithm selection is largely determined by the experience of a data scientist. However, in the case of an inexperienced data scientist, an algorithm is selected through meta-learning based on dataset characteristics. However, since the selection process is a black box, it was not possible to know on what basis the existing algorithm recommendation was derived. Accordingly, this study uses k-means cluster analysis to classify types according to data set characteristics, and to explore suitable classification algorithms and methods for resolving class imbalance. As a result of this study, four types were derived, and an appropriate class imbalance resolution method and classification algorithm were recommended according to the data set type.

• Bulletin of the Korean Chemical Society
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• v.30 no.5
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• pp.1121-1126
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• 2009

This paper describes a pattern recognition method of Magnoliae flos based on a gas chromatographic/mass spectrometric (GC/MS) analysis of the essential oil components. The botanical drug is mainly comprised of the four magnolia species (M. denudata, M. biondii, M. kobus, and M. liliflora) in Korea, although some other species are also being dealt with the drug. The GC/MS separation of the volatile components, which was extracted by the simultaneous distillation and extraction (SDE), was performed on a carbowax column (supelcowax 10; 30 m{\time}0.25 mm{\time}0.25{\mu}m$) using temperature programming. Variance in the retention times for all peaks of interests was within RSD 2% for repeated analyses (n = 9). Of the 74 essential oil components identified from the magnolia species, approximately 10 major components, which is $\alpha$-pinene, $\beta$-pinene, sabinene, myrcene, d-limonene, eucarlyptol (1,8-cineol), $\gamma$-terpinene, p-cymene, linalool, $\alpha$-terpineol, were commonly present in the four species. For statistical analysis, the original dataset was reduced to the 13 variables by Fisher criterion and factor analysis (FA). The essential oil patterns were processed by means of the multivariate statistical analysis including hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA). All samples were divided into four groups with three principal components by PCA and according to the plant origins by HCA. Thirty-three samples (23 training sets and 10 test samples to be assessed) were correctly classified into the four groups predicted by PCA. This method would provide a practical strategy for assessing the authenticity or quality of the well-known herbal drug, Magnoliae flos.

• Journal of Plant Biotechnology
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• v.47 no.3
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• pp.194-202
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• 2020

The sweetpotatoes (Ipomoea batatas) generate adventitious roots (ARs) from cut stems that develop into storage roots and make for an important means of propagation. However, few studies have investigated the hormones involved in AR development in sweetpotato. In this study, the expression patterns of hormone-related genes involved in AR formation were identified using the transcriptome data. RNA-seq data from stems grown for 0 and 3 days after cutting were analyzed. In addition, hormone-related genes were identified among differentially expressed genes (DEGs) and filtered genes, and cluster analysis was used to characterize expression patterns by function. Most hormone-related regulated genes expressed 3 days after growing the cut stems were abscisic acid (ABA)-related genes, followed by ethylene- and auxin-related genes. For ABA, the biosynthesis genes (including genes annotated to NINE-CIS-EPOXYCAROTENOID DIOXYGENASE 3 (NCED3)) and signal transduction and perception genes (including genes annotated to PROTEIN PHOSPHATASE 2Cs (PP2Cs)) tended to decrease. Expression patterns of auxin- and ethylene-related genes differed by function. These results suggest that ABA, auxin, and ethylene genes are involved in AR formation and that they may be regulated in a hormone function-dependent manner. These results contribute to the identification of hormone functions during AR formation and may contribute to understanding the mechanism of AR formation in the sweetpotato.

• Journal of the Ergonomics Society of Korea
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• v.33 no.2
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• pp.87-96
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• 2014

Objective: The present study is intended to objectively classify upper- & lower-body sitting strategies and identify the effects of gender and OPL type on the sitting strategies. Background: A sitting strategy which statistically represents comfortable driving posture can be used as a reference posture of a humanoid in virtual design and evaluation of a driver's seat. Although previous research has classified sitting strategies for driving postures in various occupant package layout (OPL) types, the existing classification methods are not objective and the factors affecting sitting strategies have not been identified. Method: Forty drivers' preferred driving postures in three different OPL types (coupe, sedan, and SUV) were measured by a motion capture system. Next, the measured driving postures were classified by K-means cluster method. Results: Sitting strategies of upper-body were classified as erect (33%), slouched (41%), and reclined (26%) postures, and those of lower-body were classified as knee bent (42%), knee extended (32%), and upper-leg lifted (26%) postures. Significant differences at ${\alpha}$ = 0.05 in the upper-body sitting strategy by gender and lower-body sitting strategy by OPL type were found. Application: Both the classified sitting strategies and the identified factors would be of use in ergonomic seat design and evaluation.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.5
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• pp.1913-1918
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• 2014

Background: The study is the first of its kind to be conducted in Saudi Arabia (KSA), aiming to analyze the effectiveness of an intervention program in improving the knowledge of oral cancer among the youth. Materials and Methods: A total of 1,051 young Saudis (57% males and 43% females with a mean age of $20.4{\pm}1.98$) were selected using multi-stage cluster sampling. Knowledge assessment was accomplished using a closed-ended questionnaire which was subjected to reliability tests. Prevalence of risk factors in relation to gender was analyzed using the chi-squared test. Effectiveness was calculated by comparing the pre- and post-intervention means, using the two-tailed paired t-test. Multiple logistic regression was employed in order to determine factors associated with awareness of risk habits, signs/symptoms and prevention of oral cancer. The significance level in this study was set at 0.05. Results: Females were seen to be more into the habit of sheesha smoking (3.3% rather than the use of other forms of risk factors. Prevalence of diverse risk factors such as cigarette smoking (20%), sheesha (15.3%), khat (27%) and shamma (9%) was seen among males. Gender and the use of modifiable risk factors among the study sample were significantly (p<0.001) associated with effectiveness of the intervention. The intervention program was highly effective (p<0.001) in improving the knowledge of oral cancer among the youth in Jazan, KSA. Multivariate analysis revealed that age and gender are the most significant factors affecting knowledge. Conclusions: The study gives a direction for further public health initiatives in this oral cancer prone region.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.