• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.525-535
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• 2006

We describe tests for detecting and locating quantitative traits loci (QTL) for traits in Hanwoo. Lod scores and a permutation test have been described. From results of a permutation test to detect QTL, we select major DNA markers of ILSTS098 microsatellite locus in Hanwoo chromosome 2 for further analysis. K-means clustering analysis applied to four traits and eight DNA markers in ILSTS098 resulted in three cluster groups. We conclude that the major DNA markers of BMS1167 microsatellite locus in Hanwoo chromosome 2 are markers 105bp, 113bp and 115bp. Finally, bootstrap testing method has been adapted to calculate confidence intervals and for finding major DNA Markers.

• 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.

• Proceedings of the Technology Innovation Conference
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• 2005.06a
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• pp.116-139
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• 2005

ICT(information and communication technology) has played a pivotal role in the world economy, and the out look for ICT has improved markedly. One of the noticeable characteristics in the ICT sector Is the global rationalization of its technology and service. Specialization on the specific ICT capability is a pressing problem for many countries. Along the line of classical innovation cluster and network studies, this paper suggests a way to find and analyze the role of core technologies on the ICT network First, technology network is constructed by using patent citation data from USPTO. Then, a couple of cluster is generated by K-means clustering technique. Finally, brokerage analysis is applied to manifest the role of principal technologies. The network visualization and some stylized facts on dynamics are briefly given altogether Based on the role and relationship of technologies across clusters, it is expected that this research could contribute to the ICT cluster formation and the vision-making for ICT specialization at the viewpoint of technology Policy.

• Journal of the Korean association of regional geographers
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• v.15 no.4
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• pp.510-523
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• 2009

Geodemographics(GD) can be defined as an analytical approach of socio-economic and behavioral data about people to investigate geographical patterns. GD is based on the assumptions that demographical and behavioral characteristics of people who live in the same neighborhood are similar and then the neighborhoods can be categorized with spatial classifications with the geographical classifications. Thus, this paper, in order to identify the applicability of the geographical classification of the GD, explores the concepts of the geodemographics into Seoul city areas with Korea census data sets that contain key characteristics of demographic profiles in the area. Then, this paper attempt to explain each area classification profile by using clustering techniques with Ward's and k-means statistical methods. For this as as as, this paper employs 2005 Census dataset released by Korea National Statistics Office and the neighborhood unit is based on Dong level, the smallest administrative boundary unit in Korea. After selecting and standardizing variables, several areas are categorized by the cluster techniques into 13, this paps as distinctive cluster profiles. These cluster profiles are used to cthite a short description and expand on the cluster names. Finally, the results of the classification propose a reasonable judgement for target area types which benefits for the people who make a spatial decision for their spatial problem-solving.

• 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 Navigation and Port Research
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• v.27 no.3
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• pp.315-320
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• 2003

Since the beginning of open-door policy, China has been making rapid annual growth with an average 10% economic development. And due to this rapid growth, cargo volumes via ports have been also rapidly increased, and accordingly, current China government has intensively invested in port development. Further, this development project is significantly big scale, compared with those project which Korea and Japan have. Thus, China is beginning to threaten Korean ports, especially Busan port which try to be a hub port in Northeast Asia. For this reason, it has been very important issue for Korea and Busan port to investigate or analyze Chinese ports based on empirical data. Especially, although various studies related to Shanghai and Hong Kong have been conducted, the competitiveness of overall Chinese major ports has been little studied. In this paper, we analyzed competitiveness level of eight Chinese ports with capabilities as container terminal, based on reliable sources. From data analysis, eight Chinese ports were classified into four groups according to competitiveness level. Rankings among four clusters based on competitiveness level are cluster(Hone Kong), cluster C(Shanghai), cluster A(Qingdao, Tianjin, and Yantian) and cluster D(Dalian, Shekou, and Xiamen).

• Proceedings of the Korea Society of Costume Conference
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• 2003.10a
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• pp.82-82
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• 2003

The purposes of this study were to examine the purchasing and using behaviors of cosmetics according to women's consumption propensity. A self administered Questionnaire was used and the subjects were 600 women of 20s and 30s in Jeonbuk Province. The data collecting periods were July, 2003. Means, percentages, and frequency were calculated. And factor analysis, cluster analysis, one-way ANOVA, and Chi-square test were done by use of SPSS PC (Ver. 10.0).

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.209-215
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• 2015

Fuzzy c-means method is typical soft clustering, and requires a degree of membership that indicates the degree of belonging to each cluster at the time of clustering. Parameter values greater than 1 and less than 2 have been used by convention. According to the proposed data-generation scheme and the simulation results, some behaviors in the degree of "fuzziness" was derived.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.1-7
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• 2020

Clustering methods essentially take a two-step approach; extracting feature vectors for dimensionality reduction and then employing clustering algorithm on the extracted feature vectors. However, for clustering images, the traditional clustering methods such as stacked auto-encoder based k-means are not effective since they tend to ignore the local information. In this paper, we propose a method first to effectively reduce data dimensionality using convolutional auto-encoder to capture and reflect the local information and then to accurately cluster similar data samples by using a hierarchical clustering approach. The experimental results confirm that the clustering results are improved by using the proposed model in terms of clustering accuracy and normalized mutual information.

• Journal of Family Resource Management and Policy Review
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• v.13 no.2
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• pp.129-151
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• 2009

The first purpose of this study was to reveal the types of shopping value of college students. The second purpose was to examine the change in the perception and management behaviors related to credit cards according to the types of shopping value. The third purpose was to examine the effects of shopping value on perception and management behaviors on credit cards. The data were collected from 392 college students in Seoul by a self-administered questionnaire. Analyses including frequency, mean, factor analysis, Cronbach's alpha, Pearson's correlation analysis, Crosstabulation analysis, analysis of variance, K-means Cluster analysis and Multiple linear regression were conducted using SPSS WIN12.0. The major findings were as follows. First, college students can be categorized into 3 types of shopping values by K-means Cluster analysis of 14 items. The groups were entitled the hedonistic shopping value, the utilitarian shopping value, and the saving shopping value. Second, positive perception and management behaviors related to credit cards were different depending on the types of shopping value. The hedonistic shopping value group had a higher level of positive perception of credit cards and a lower level of credit card management, compared with the other groups. The saving shopping value group had higher levels of both positive perception and management of credit cards. Among the three groups, the utilitarian shopping group had the lowest level of positive perception of credit cards, despite having ahigher level of credit card management. Lastly, the most effective variance on credit card management was the utilitarian shopping value. These results suggest that a healthy shopping value is very important for having a healthy perception and management of credit cards, because shopping value is a critical variance to affect perception and management of credit cards.