• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.93-97
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• 2002

In this paper, we propose personalized recommendation techniques based on multidimensional scaling (MDS) method for Business to Consumer Electronic Commerce. The multidimensional scaling method is traditionally used in marketing domain for analyzing customers' perceptional differences about brands and products. In this study, using purchase history data, customers in learning dataset are assigned to specific product categories, and after then using MDS a positioning map is generated to map product categories and alternative advertisements. The positioning map will be used to select personalized advertisement in real time situation. In this paper, we suggest the detail design of personalized recommendation method using MDS and compare with other approaches (random approach, collaborative filtering, and TOP3 approach)

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.665-676
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• 2014

As social media draws attention as a business tool, organizations, large or small, are trying to exploit social media in their business. However, lack of understanding the characteristics of each social media led them to develop a naive strategy for dealing with social media. Thus, this study aims to deepen the understanding by comparatively analyzing how social media users perceive (the image of) each social media. Facebook, Twitter, YouTube, Blogs, Communities and Cyworld were chosen for our study and data from 132 respondents were analyzed using multidimensional scaling technique. The results show that there are meaningful differences in users' perception of social media attributes, which are grouped into four; information feature, motivation, promotion tool, usability. It is also analyzed whether such differences can be found between male and female users. (Such differences are also analyzed in both male and female users' perceptions.) Further, we discuss some implications of the research results for both practitioners and researchers.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.05a
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• pp.27-28
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• 2011

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1135-1143
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• 2007

The singular value decomposition and the spectral decomposition are the useful methods in the area of matrix computation for multivariate techniques such as principal component analysis and multidimensional scaling. These techniques aim to find a simpler geometric structure for the data points. The singular value decomposition and the spectral decomposition are the methods being used in these techniques for this purpose. In this paper, the singular value decomposition and the spectral decomposition are compared.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.567-578
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• 2017

Grouped multivariate data can be tested for differences between two or more groups using multivariate analysis of variance (MANOVA). However, this method cannot be used if several assumptions of MANOVA are violated. In this case, multidimensional scaling (MDS) and analysis of distance (AOD) can be applied to grouped dissimilarities based on the various distances. A permutation test is a non-parametric method that can also be used to test differences between groups. MDS is used to calculate the coordinates of observations from dissimilarities and AOD is useful for finding group structure using the coordinates. In particular, AOD is mathematically associated with MANOVA if using the Euclidean distance when computing dissimilarities. In this paper, we study the between and within group structure by applying MDS and AOD to the grouped dissimilarities. In addition, we propose a new test statistic using the group structure for the permutation test. Finally, we investigate the relationship between AOD and MANOVA from dissimilarities based on the Euclidean distance.

• Journal of the Korean Society of Food Culture
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• v.15 no.1
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• pp.51-58
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• 2000

This study is designed to 1) understand customers' choice behavior and preference of foodservices in campus and 2) provide recommendation on management strategies for university foodservice manager. Individual interview and focus group interview were used to identify important selection attributes. The questionnaire was developed and distributed to 480 Yonsei university students and statistical data analysis was completed using SPSS WIN/7.5 for descriptive analysis, multidimensional scaling and conjoint analysis. The results of this study were summarized as follows: Students evaluated four foodservices in different ways, and strength/weakness points could be identified from the evaluation patterns. Most students(51.1%) were frequently used 'A' foodservice, though they preferred other foodservices, and cost, mainly, caused the difference. Perceptual map from multidimensional scaling showed that preference and patronage were close with different attributes. Cost was most relatively important attribute to select foodservice in campus from conjoint analysis. Therefore, relative importance of attributes should be considered in customer preference survey for constructing management plan.

• Journal of The Korean Association For Science Education
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• v.33 no.3
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• pp.650-663
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• 2013

Multidimensional scaling can be used to identify relationships among concepts, revealing the structure of the cognitive framework by measuring distances within perceptual maps. The current study sought to examine the relationships among concepts related to photosynthesis in 2,844 $3^{rd}-11^{th}$ grade science students. The questionnaire included items on 'location,' 'products,' 'reactants,' and 'environmental factors', presenting images related to each theme. Students provided responses corresponding to particular topics, and reported the extent to which the concept was related to the topic on a scale from 1 to 30. The survey results were as follows: first, students were not able to clearly distinguish between or understand the four main topics. Second, students organized their cognitive structures by closely associating related concepts after learning. Third, the presented concepts revealed a mixture of scientific and non-scientific concepts, suggesting that students needed to clearly distinguish the preconceptions through which they organized concepts, so that they are suitable for cognitive structures based on learning. Furthermore, non-scientific concepts within perceptions were consistently maintained throughout learning, affecting the proximity of scientific concepts.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.15 no.2
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• pp.169-174
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• 2011

Objectives: Seven Emotions consist of Joy(喜), Anger(怒), Anxiety(憂), Thought(思), Sorrow(悲), Fear(恐), Fright(驚). If Seven Emotions are excessive, their extreme mental stimulations cause physical illness. The aim of the research is to make a proposal on the concept of Seven Emotions by a statistical comparison between guibi-tang questionnaire and health related questionnaires. Methods: We studied the similarities among three factors of guibi-tang questionnaire and three health related questionnaires(subjective symptoms of fatigue test, beck depression inventory, state-trait anxiety inventory, etc.) using multidimensional scaling. Results and Conclusions: 1. Physical-Emotion Dimension and Chronic-Acute Dimension were labelled in two-dimensional solution. 2. Seven Emotions and Fatigue have a high correlation. 3. Seven Emotions and Trait-Anxiety have a high correlation.

• Journal of Biomedical Engineering Research
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• v.31 no.2
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• pp.134-140
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• 2010

Readily available high resolution brain MRI scans allow detailed visualization of the brain structures. Researchers have focused on developing methods to quantify shape differences specific to diseased scans. We have developed a novel method to quantify shape information for a specific population based on Multidimensional scaling(MDS). MDS is a well known tool in statistics and here we apply this classical tool to quantify shape change. Distance measures are required in MDS which are computed from pair-wise image registrations of the training set. Registration step establishes spatial correspondence among scans so that they can be compared in the same spatial framework. One benefit of our method is that it is quite robust to errors in registrations. Applying our method to 13 brain MRI showed clear separation between normal and diseased (Cushing's syndrome). Intentionally perturbing the image registration results did not significantly affect the separability of two clusters. We have developed a novel method to quantify shape based on MDS, which is robust to image mis-registration.

• Journal of Korean Society of Transportation
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• v.4 no.1
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• pp.12-27
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• 1986

This study examined user preferences toward transportation modes in Seoul. Two multidimensional scaling models, the ideal point and vector models, were applied to data on mode preferences of 114 adults in the metropolitan area. While both models produced fairly similar results, the vector model performed slightly better than the other in terms of interpretability of the results. The transport attributes elicited are comfort, flexibility, travel cost, travel time, privacy, and safety; among which comfort is salient most. The comfort variable is a multi-faceted attribute in nature. The variations of attribute preferences are most significant between the gender groups as well as worker/nonworker groups. In particular, male workers, female workers and female nonworkers form three distinctive market segments. An unidimensional scaling of the preference data reveals that subway, auto-driver, and subscription bus modes are preferred most, whereas motorcycle and bicycle least. The other modes of express bus, taxt, auto-passenger, bus and walk rank intermediately. An examination of how preference orders vary among modal groups hints that users align their stated attitudes to their choice in order to reduce cognitive dissonance.