• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.3
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• pp.417-428
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• 2018

The purpose of this study is to raise the necessity of ensuring sustainability of water infrastructures in economic, social and environmental sectors by using index that evaluates the sustainability centering on water supply and wastewater utilities. This study identified sub-indexes that should be stressed among different indexes in economic, social and environmental aspects and those indexes were compared by each clusters of cities. The principal component analysis was used to calculate the weights of the sub-indexes, and the k-mean cluster analysis was conducted to classify the clusters. As a result of the weighting analysis, financial independence, service revenue ratio, subsidy ratio, population coverage ratio, deterioration, stream/river ecosystem health and river water quality were found to be the major variables in assessing sustainability. Cities were then classified into two groups using the k-mean cluster analysis. The overall sustainability scored high in the economic sector was relatively satisfactory, but it was necessary to improve the environmental sustainability. The group with relatively good environmental sustainability showed low score in the overall sustainability and required improvements in the economic sector.

• Journal of Fashion Business
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• v.9 no.5
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• pp.1-14
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• 2005

The purpose of this study was to analysis the perception of men's wear brands (Intermezzo and Rogatis), for developing the possibility & strategy of the nichi-market in men's wear market for the apparel marketers and manufactures. For this study, the data obtained from 312 respondents were analyzed by descriptive statistics, ANOVA. The results from the study were as follow ; The perception of the 2 brand images revealed that Intermezzo accounted for 79.8% of the frequencies, and Rogatis accounted for 99%. Also, results revealed the total evaluation of Intermezzo accounted for 3.86 of the mean rated on 5 point Likert-type scales in the 9 features, and Rogatis accounted for 3.28. And then, results revealed that there were signifiant differences in 2 cluster of Rogatis that the purchasing cluster accounted for 3.46 of the mean, and the perceiving cluster accounted for 3.07. The brand images of Intermezzo and Rogatis were evaluated and rated on 5 point Likert-type scales of 17 pair adjectives. As a results, the image characteristic with Intermezzo was considered with more dynamic, trendy than the image characteristic with Rogatis. Also, results revealed that The Image with Intermezzo was considered with urban, lively, chic, modern, and sophsticated image-features, and the Image with Rogatis were evaluated mannish, urban, sophsticated, luxury, and static image-features.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.5
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• pp.513-525
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• 2016

To propose an effective ensemble methods in predicting $PM_{10}$ concentration, six experiments were designed by different ensemble average methods (e.g., non-weighted, single weighted, and cluster weighted methods). The single weighted method was calculated the weighted value using both multiple regression analysis and singular value decomposition and the cluster weighted method was estimated the weighted value based on temperature, relative humidity, and wind component using multiple regression analysis. The effects of ensemble average methods were significantly better in weighted average than non-weight. The results of ensemble experiments using weighted average methods were distinguished according to methods calculating the weighted value. The single weighted average method using multiple regression analysis showed the highest accuracy for hourly $PM_{10}$ concentration, and the cluster weighted average method based on relative humidity showed the highest accuracy for daily mean $PM_{10}$ concentration. However, the result of ensemble spread analysis showed better reliability in the single weighted average method than the cluster weighted average method based on relative humidity. Thus, the single weighted average method was the most effective method in this study case.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1072-1077
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• 2017

Mean-reverting analysis refers to a way of estimating the underlining tendency after new data has evoked the variation in the equilibrium state. In this paper, we propose a new method to interpret the specular portraits of Premature Ventricular Contraction(PVC) arrhythmia by applying K-means unsupervised learning algorithm on electrocardiogram(ECG) data. Aiming at this purpose, we applied a mean-reverting model to analyse Heart Rate Variability(HRV) in terms of the modified poincare plot by considering PVC rhythm as the component of disrupting the homeostasis state. Based on our experimental tests on MIT-BIH ECG database, we can find the fact that the specular patterns portraited by K-means clustering on mean-reverting HRV data can be more clearly visible and the Euclidean metric can be used to identify the discrepancy between the normal sinus rhythm and PVC beats by the relative distance among cluster-centroids.

• The korean journal of orthodontics
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• v.45 no.2
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• pp.74-81
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• 2015

Objective: The purposes of this study were 1) to classify lingual dental arch form types based on the lingual bracket points and 2) to provide a new lingual arch form template based on this classification for clinical application through the analysis of three-dimensional virtual models of normal occlusion sample. Methods: Maxillary and mandibular casts of 115 young adults with normal occlusion were scanned in their occluded positions and lingual bracket points were digitized on the virtual models by using Rapidform 2006 software. Sixty-eight cases (dataset 1) were used in K-means cluster analysis to classify arch forms with intercanine, interpremolar and intermolar widths and width/depth ratios as determinants. The best-fit curves of the mean arch forms were generated. The remaining cases (dataset 2) were mapped into the obtained clusters and a multivariate test was performed to assess the differences between the clusters. Results: Four-cluster classification demonstrated maximum inter-cluster distance. Wide, narrow, tapering, and ovoid types were described according to the intercanine and intermolar widths and their best-fit curves were depicted. No significant differences in arch depths existed among the clusters. Strong to moderate correlations were found between maxillary and mandibular arch widths. Conclusions: Lingual arch forms have been classified into 4 types based on their anterior and posterior dimensions. A template of the 4 arch forms has been depicted. Three-dimensional analysis of the lingual bracket points provides more accurate identification of arch form and, consequently, archwire selection.

• 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.

• Journal of Fashion Business
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• v.2 no.2
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• pp.37-45
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• 1998

The purpose of this study is to survey and classify the differences of the perceived fashion risk according to the apparels and accessories that consumers purchased. 243 ungraduate were separated into three groups and asked to rate 15 fashion risk concerns about each item on 5-point scale. The number of item was 103 in the total of the three group. Data were analyzed by using Mean, SO, ANOVA, Factor Analysis, Cluster Analysis, Cronbach $\alpha$ with SAS program. The result of this study was high perceived risk in leather Jacket, suit, long coat, sunglasses. The most important factor of the perceived risk structure in the fashion goods was about the perceived risk perception of others. The apparels and accessories which completes the dress were classified into the same cluster. Consumers don't perceive the fashion goods independently, but they make much of the combination of other items.

• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.15-24
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• 2010

Regional frequency analysis is often used to overcome the limitation of point frequency analysis to estimate probability rainfall depths. However, point frequency analysis is still used in drought analyses. This study proposed a practical method to categorize the homogeneous regions of drought characteristics for the analyses of regional characteristics of droughts in Korea. Using rainfall data from 58 observation stations managed by the Korea Meteorological Administration, this study calculated drought attributes, i.e., mean drought indices for various durations using the Standardized Precipitation Index (SPI) and drought severities expressed by durations, depth, and intensity. The drought attributes provided useful information for categorizing stations into the hydrological homogeneous regions. This study introduced a cluster analysis with K-means techniques to group observation stations. The cluster analysis grouped observation stations into 6 regions in Korea. The data in the hydrological homogeneous region would be used in spatial analysis of drought characteristics and drought regional frequency analysis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.183-188
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• 2013

A variety of cluster analysis techniques prerequisite to cluster objects having similar characteristics in data mining. But the clustering of those algorithms have lots of difficulties in dealing with categorical data within the databases. The imprecise handling of uncertainty within categorical data in the clustering process stems from the only algebraic logic of rough set, resulting in the degradation of stability and effectiveness. This paper proposes a information-theoretic rough entropy(RE) by taking into account the dependency of attributes and proposes a technique called min-mean-mean roughness(MMMR) for selecting clustering attribute. We analyze and compare the performance of the proposed technique with K-means, fuzzy techniques and other standard deviation roughness methods based on ZOO dataset. The results verify the better performance of the proposed approach.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.13 no.2
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• pp.304-311
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• 2006

Purpose: This study was done to examine the relation of self-reported adult attachment style, perceived parental rearing styles and anger in undergraduate students. Method: The six hundred and fifty undergraduate students participating in this descriptive correlational design study were conveniently sampled from K University and S College located in Seoul, Korea. The instruments were Spielberger's state-trait anger expression inventory - Korean version(Chon, Han, Lee & Spielberger, 1997), the instrument for measuring attachment styles by Hazen and Shaver (1987), and Hong's instrument for measuring parental rearing style(2001). Data were analyzed by descriptive statistics, t-test, $X^2-test$, ANOVA, and cluster analysis using pc-SAS(version 8.0e) program. Results: The mean scores for trait anger and anger-in were higher in undergraduate students with insecure and ambivalent attachment style compared to students with a secure attachment style. The mean score for anger-control was highest in undergraduate students with a secure attachment style. The parental rearing styles by cluster analysis were grouped as Neglect, Permissive, Democratic, and Protective-control. The mean scores for trait anger, anger-in, and anger-out were higher in undergraduate students with 'Neglect' parental rearing style than in those with 'Democratic' and 'Protective-control' rearing styles. Conclusion: Trait anger and anger expression might be related to an attachment style and/or a parental rearing style.