• 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 KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.427-431
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• 2010

Multidimensional scaling (MDS) is a widely used method for dimensionality reduction, of which purpose is to represent high-dimensional data in a low-dimensional space while preserving distances among objects as much as possible. MDS has mainly been applied to data visualization and feature selection. Among various MDS methods, the classical MDS is not readily applicable to data which has large numbers of objects, on normal desktop computers due to its computational complexity. More precisely, it needs to solve eigenpair problems on dissimilarity matrices based on Euclidean distance. Thus, running time and required memory of the classical MDS highly increase as n (the number of objects) grows up, restricting its use in large-scale domains. In this paper, we propose an efficient approximation algorithm for the classical MDS based on divide-and-conquer and CUDA. Through a set of experiments, we show that our approach is highly efficient and effective for analysis and visualization of data consisting of several thousands of objects.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1171-1180
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• 2015

Multidimensional scaling (MDS) is a graphical technique of multivariate analysis to display dissimilarities among individuals into low-dimensional space. We often have two kinds of MDS which are metric MDS and non-metric MDS. Metric MDS can be applied to quantitative data; however, we need additional information about variables because it only shows relationships among individuals. Gower (1992) proposed a method that can represent variable information using trajectories of the pseudo-points for quantitative variables on the metric MDS space. We will call his method a 'replacement method'. However, the trajectory can not be represented even though metric MDS can be applied to binary data when we apply his method to binary data. Therefore, we propose a method to represent information of binary variables using pseudo-points called a 'partition method'. The proposed method partitions pseudo-points, accounting both the rate of zeroes and ones. Our metric MDS using the proposed partition method can show the relationship between individuals and variables for binary data.

• ALGAE
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• v.31 no.1
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• pp.1-16
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• 2016

Paralia species have been frequently reported as P. sulcata in Korea, despite the species diversity within the genus. To understand the species diversity of Paralia in Korea, we collected phytoplankton samples at 79 sites from April 2006 to April 2015. Five Paralia species, P. fenestrata, P. guyana, P. marina, P. cf. obscura, and P. sulcata, were observed during this study, and we described their fine structure in terms of quantitative and qualitative morphological characteristics. To provide additional criteria to identify Paralia species more clearly, we morphometrically analysed four quantitative characteristics on valve diameter: pervalvar axis / diameter, internal linking spines / diameter, marginal linking spines / diameter, and fenestrae/diameter using non-metric multidimensional scaling (MDS). MDS analysis distinguished four Paralia species: P. guyana, P. marina, P. cf. obscura, and P. sulcata, with the exception of P. fenestrata. This new approach in using morphometric analysis is useful for the accurate identification of Paralia species.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.517-528
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• 2018

We provide R scripts to detect outliers in multivariate data and visualization. Detecting outliers is provided using three approaches 1) Robust Mahalanobis distance, 2) High Dimensional data, 3) density-based approach methods. We use the following techniques to visualize detected potential outliers 1) multidimensional scaling (MDS) and minimal spanning tree (MST) with k-means clustering, 2) MDS with fviz cluster, 3) principal component analysis (PCA) with fviz cluster. For real data sets, we use MLB pitching data including Ryu, Hyun-jin in 2013 and 2014. The developed R scripts can be downloaded at "http://www.knou.ac.kr/~sskim/ddpoutlier.html" (R scripts and also R package can be downloaded here).

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.67-75
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• 2018

Multidimensional scaling (MDS) is an exploratory analysis of multivariate data to represent the dissimilarity among objects in the geometric low-dimensional space. However, a general MDS map only shows the information of objects without any information about variables. In this study, we used MDS based on the algorithm of Torgerson (Theory and Methods of Scaling, Wiley, 1958) to visualize some clusters of objects in categorical data. For this, we convert given data into a multiple indicator matrix. Additionally, we added the information of levels for each categorical variable on the MDS map by applying the partition method of Shin et al. (Korean Journal of Applied Statistics, 28, 1171-1180, 2015). Therefore, we can find information on the similarity among objects as well as find associations among categorical variables using the proposed MDS map.

• The Korea Journal of Herbology
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• v.35 no.3
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• pp.33-45
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• 2020

Objective : The present study aimed to evaluate the change of the content of 7 active components in decoctions produced by various proportional pairs of Coptis chinensis Franch and Scutellaria baicalensis Georgi in 'Mixed decoction (MD)' and 'Single decoction mixture (SDM)'. Methods : The samples of MDs were prepared by decocting C. chinensis : S. baicalensis with the ratios of 10 g:10 g, 10 g:20 g, and 20 g:10 g. Those of SDMs were prepared by blending each single decoction from C. chinensis and S. Baicalensis with the ratios of 1:1, 1:2, and 2:1. The samples were evaluated by high-performance liquid chromatography with statistical analyses. Results : The analytical methods, which were optimized and validated, were reliably applied to present research. The content of all components in both MDs and SDMs at C. chinensis : S. baicalensis = 1:1 ratio were reduced compared with single herb decoction. The components from each compositional herb in MDs were proportionally increased with the ratio of original herb increased, but inversely proportional to paired herb. The contents of components in MDs were significantly lower than those in SDMs at all ratios, except for high content of baicalin at C. chinensis : S. baicalensis = 2:1. Conclusion : It was concluded that MDs and SDMs as well as the proportions of herbs could affect the contents of the components from original herbal medicines. These results provide the information for the quality control of herbal medicine combined C. chinensis with S. baicalensis.

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.45-70
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• 2012

There have been many methods and algorithms proposed for multidimensional scaling to mapping the relationships between data objects into low dimensional space. But traditional techniques, such as PROXSCAL or ALSCAL, were found not effective for visualizing the proximities between objects and the structure of clusters of large data sets have more than 50 objects. The CLUSCAL(CLUster-oriented SCALing) technique introduced in this paper differs from them especially in that it uses cluster structure of input data set. The CLUSCAL procedure was tested and evaluated on two data sets, one is 50 authors co-citation data and the other is 85 words co-occurrence data. The results can be regarded as promising the usefulness of CLUSCAL method especially in identifying clusters on MDS maps.

• The Journal of Fisheries Business Administration
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• v.48 no.2
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• pp.53-66
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• 2017

More than 90% of cultured fish is consumed by sliced raw fish which is usually an eating out menu in South Korea. In order to develop the aquaculture industry in Korea, It is very important to know whether consumers can differentiate each species or not and how consumers recognize each species by certain criteria. for example taste, seasonal preference. The purpose of this study is to understand the competitive relationship through positioning studies of each species by the selection attributes. A total of 221 consumers were surveyed in korea. This study adapted multidimensional scaling(MDS) analysis to explore how consumers position sliced raw fish species based on selection attributes. This study has produced perceptual maps of sliced raw fish market. Empirical data was collected from sliced raw fish consumers in Korea. The results of MDS analysis reveal that 7 species are divided into 3 groups(flat fish, black rock fish), (red sea bream, salmon, tuna), (sea bass, gray mullet). In this study flat fish and black rock fish are perceived as safe, familiar, good value species. Red seabream, salmon, tuna are perceived as luxurious species. Sea bass and gray mullet are perceived as unfamiliar species.

• Journal of the Korean association of regional geographers
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• v.15 no.2
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• pp.250-260
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• 2009

The purpose of the research is to investigate the possibility of the tourism resources development preference of sport leisure event activity of participation as one use MDS and clustering market character. An empirical research has been undertaken the questionnaire had be distributed to the whole country inline marathon races participation and there were 330 responses. The research was conducted by using statistical packages of SPSS program. As research methods factor analysis and cluster analysis were also employed. Three distinct cluster groups were categorized by their characteristics: 'money acquirement participation', 'self realization moderators', 'self realization enthusiasts', and there was differences among segmented groups in terms of their affecting factors to the tourism resources development preference. These findings suggested that there were need to tourism resources development for different segmented groups of sports leisure event activity selection attributes and each group pursued different satisfaction.