• 한국항해항만학회지
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• 제27권3호
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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).

• 한국경영과학회지
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• 제34권2호
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• pp.35-54
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• 2009

The recent economic crisis not only reduces the profit of department stores but also incurs the significance losses caused by the increasing late-payment rate of credit cards. Under this pressure, the scope of credit prediction needs to be broadened from the simple prediction of whether this customer has a good credit or not to the accurate prediction of how much profit can be gained from this customer. This study classifies the delinquent customers of credit card in a Korean department store into homogeneous clusters. Using this information, this study analyzes the repayment patterns for each cluster and develops the credit prediction system to manage the delinquent customers. The model presented by this study uses Kohonen network, which is one of artificial neural networks of data mining technique, to cluster the credit delinquent customers into clusters. Cox proportional hazard model is also used, which is one of survival analysis used in medical statistics, to analyze the repayment patterns of the delinquent customers in each cluster. The presented model estimates the repayment period of delinquent customers for each cluster and introduces the influencing variables on the repayment pattern prediction. Although there are some differences among clusters, the variables about the purchasing frequency in a month and the average number of installment repayment are the most predictive variables for the repayment pattern. The accuracy of the presented system leaches 97.5%.

• Journal of Microbiology
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• 제33권2호
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• pp.95-102
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• 1995

A probability identification matrix of acidophilic Streptomyces was constructed. The phenetic data of the strains were derived from numerical classification described by Seong et al. The minimum number of diagnostic characters was determined using computer programs for calculation of different separation indices. The resulting matrix consisted of 25 clusters versus 53 characters. Theoretical evaluation of this matrix was achieved by estimating the chuster overlap and the identification scores for the Hypothetical Median Organisms (HMO) and for the representatives of each cluster. Cluster overlap was found to be relatively small. Identification scores for the HMO and the randomly selected representatives of each cluster were satisfactory. The matrix was assessed practically by applying the matrix to the identification of unknown isolates. Of the unknown isolates, 71.9% were clearly identified to one of eight clusters. The numerical classification data was also used to design a selective isolation medium for antibiotic-producing organisms. Four chemical substances including 2 antibiotics were determined by the DLACHAR program as diagnostic for the isolation of target organisms which have antimicrobial activity against Micrococcus luteus. It was possible to detect the increased rate of selective isolation on the synthesized medium. Theresults show that the numerical phenetic data can be applied to a variety of purposes, such as construction of identification matrix and selective isolation medium for acidophilic antinomycetes.

• 한국콘텐츠학회논문지
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• 제9권10호
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• pp.398-406
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• 2009

본 연구의 목적은 관광행동에 따른 여행상품속성의 차이를 살펴봄으로써 여행업계가 더 주력해야 할 여행상품을 판매하기 위한 마케팅 전략을 제시하는 것에 도움을 주고자 하는 것이다. 본 연구를 위해 군집분석과 ANOVA분석이 이용되었는데, 군집분석 결과 4개의 군집으로 나타났다. '합리적 관광형', '약한 과시적 관광형', '가치적 관광형'과 '과시적 가치적 관광형'으로 분류되었다. ANOVA분석 결과 여행서비스와 오락을 제외한 교통 숙박시설, 관광매력물, 쇼핑, 식사에서는 관광행동에 있어 군집들이 차이를 보이는 것으로 나타났다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.976-990
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• 2020

The k-means algorithm is widely used in academia and industry due to easy and simple implementation, enabling fast learning for complex datasets. However, k-means struggles to classify datasets without prior knowledge of specific domains. We proposed the repulsive k-means (RK-means) algorithm in a previous study to improve the k-means algorithm, using the repulsive force concept, which allows deleting unnecessary cluster centroids. Accordingly, the RK-means enables to classifying of a dataset without domain knowledge. However, three main problems remain. The RK-means algorithm includes a cluster repulsive force offset, for clusters confined in other clusters, which can cause cluster locking; we were unable to prove RK-means provided optimal convergence in the previous study; and RK-means shown better performance only normalize term and weight. Therefore, this paper proposes the advanced RK-means (ARK-means) algorithm to resolve the RK-means problems. We establish an initialization strategy for deploying cluster centroids and define a metric for the ARK-means algorithm. Finally, we redefine the mass and normalize terms to close to the general dataset. We show ARK-means feasibility experimentally using blob and iris datasets. Experiment results verify the proposed ARK-means algorithm provides better performance than k-means, k'-means, and RK-means.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2002년도 춘계학술대회 논문집
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• pp.266-269
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• 2002

This paper explores a fundamental study of acquiring the users' KANSEI information regarding the recognition of shape models. Since there are many differences such as background differences and knowledge differences among users, they will produce different evaluations based on their KANSEI even when an identical shape model is presented. Cluster analysis is proved to be available for catching a group tendency and for constructing a mapping relation between a description of the shape model and the HANSEl database. In order to investigate an analogical relation and a mutual influence in our consciousness, first, we made a questionnaire that asked subjects to represent images having different colors and shape cones by using 4 pairs of adjectives (KANSEI words). Next, based on the cluster analysis of the questionnaire using a fuzzy set theory, we proposed a hypothesis showing how the analogical relation and the mutual influence work in our mind while viewing the shape models. Furthermore, how the properties of KANSEI depend on their descriptions was also investigated by virtue of the cluster analysis. This work will be valuable to construct a personal KANSEI database regarding the Shape Model Processing System.

• 천문학회보
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• 제39권1호
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• pp.46.2-46.2
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• 2014

Galaxy morphology is involved complex effects of both secular and non-secular evolution of galaxies. Although it is a final product of a galaxy evolution, it may give a clue for the process that the galaxy suffer. Galaxy clusters are the sites where the most massive galaxies are found, and the most dramatic merger histories are embedded. Morphology study in nearby universe, e.g. Virgo cluster, is well established, but for clusters at z ~ 0.1 it is only focused on bright galaxies due to observational limits. Our optical deep imaging of 14 Abell clusters at z = 0.014 - 0.16 using IMACS f/2 on a Magellan Badde 6.5-m telescope and MegaCam on a 3.8-m CFHT enable to classify detailed morphology. For the galaxies in our data, we investigated their morphology with several criteria related to secular or merger related evolution. Our research on detailed morphology of thousands of galaxies through deep imaging would give a general census of cluster galaxies and help to estimate the evolution of cluster galaxies.

• 문화기술의 융합
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• 제7권3호
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• pp.481-486
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• 2021

기계학습 알고리즘은 기준 함수를 채택하여 데이터를 처리하고 학습 모델을 유도한다. 군집분석에서 사용하는 기준 함수는 어떤 형태로든지 선호성을 내포하게 되고 이를 통해 유사한 데이터끼리 묶어 준 후 이를 구성하는 변수와 값들을 특정하여 군집을 정의하게 된다. 군집분석에서 사용하는 카테고리 유용도와 분할 유용도 점수가 군집분석 결과물에 어떤 영향을 주는지를 파악하고 이들이 결과에 어떤 편향성으로 이어지는지를 분석한다. 본 연구는 군집분석에 사용되는 기준 함수의 특성에 따라 결과에 미치는 영향을 파악하기 위해 여러 데이터 세트를 이용해 실험하고 결과를 평가한다.

• Journal of Forest and Environmental Science
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• 제38권1호
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• pp.12-20
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• 2022

Pongamia (Pongamia pinnata L.) as a source of non-edible oil, is potential tree species for biodiesel production. For several reasons, both technical and economical, the potential of P. pinnata is far from being realized. The exploitation of genetic diversity for crop improvement has been the major driving force for the exploration and ex situ/in situ conservation of plant genetic resources. However, P. pinnata improvement for high oil and seed production is not achieved because of unsystematic way of tree improvement. Performance of P. pinnata planted by Karnataka Forest Department was assessed based on yield potential by collecting 157 clones out of 264 clones established by Karnataka Forest Department research wing under different research circles/ranges. It was evident that the all the seed and pod traits were significantly different. Further, selection of superior germplasm based on oil and pod/seed parameters was achieved by application of Mahalanobis statistics and Tocher's technique. On the basis of D² values for all possible 253 pairs of populations the 157 genotypes were grouped into 28 clusters. The clustering pattern showed that geographical diversity is not necessarily related to genetic diversity. Cluster means indicated a wide range of variation for all the pod and seed traits. The best cluster having total oil content of more than 34.9% with 100 seed weight of above 125 g viz. Cluster I, II, III, IX, XV, XIX, XXI, XXIII, XXVI and XXVII were selected for clonal propagation.

• 한국콘텐츠학회논문지
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• 제7권1호
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• pp.155-160
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• 2007

SCORM에서 학습 자원은 공유 가능 콘텐츠 객체 또는 하나 이상의 애셋(asset)으로 구성된다. 이러닝 환경에서 애셋을 신속하게 검색하고 재사용할 수 있는 저장 방법이 필요하지만 아직 관련된 연구가 거의 없다. 본 논문에서는 클러스터에 기반을 둔 애셋의 저장 방법을 제안하고 수학적으로 정형화하여 정의하였다. 또한, 애셋을 평가하는 기준과 각 애셋을 평가하는 절차를 제시하였다. 실험을 통하여 제안한 클러스터저장 방법에 기반을 둔 검색이 텍스트 카테고리화에 기반한 검색보다 처리시간과 정확도 측면에서 성능이 우수함을 보였다.