• The Journal of Information Technology
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• v.5 no.1
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• pp.39-50
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• 2002

In order for Internet Shopping Mall to be successful, it must have a theoretical framework that considers the characteristics of the Internet shopping mall users. But as of yet, there is virtually no such framework. The purpose of this study is to provide internet shopping mall in our country with the suggestive point in the marketing strategy for coping with our continuously changing environment on the basis of data gained through the following results of my research work. Hitting upon this situation, this research intends to classified by Cluster Analysis the I-VALS in Internet Shopping Mall. As a result, the purpose of this research is to supply useful data on establishing Department Internet Showing Mall. We used two methods. The first method was directly distributing questionnaires to cyberconsumers by use of one researcher who has been trained in people. The second method was sending E-mails, Fax, Mail designed to induce participation in questionnaires. Through these methods, we were able to obtain participation of people from across the country. Using 179 responses, we derived statistics by means of SPSS Win Version 10.0. The result of Empirical analysis is as follow : first, to pick out 9 factors from I-VALS of users of internet shopping mall. Second, as the result of there was a statistically significant difference between the I-VALS of internet shopping mall. As the result of the Cluster Analysis, I could see there classified 3 factors from I-VALS of users of internet shopping mall. We have know segmentation of internet shopping malls on the standard of user life, and partially significant difference with respect to using internet shopping mall information of I-VALS.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.70-70
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• 2011

기후변화 연구는 불확실한 미래를 전망하는 과정이므로 '불확실성'은 모든 기후변화 영향평가의 키워드임에 분명하다. 하지만 불확실성 평가를 위해 IPCC에서 제공되고 있는 수많은 GCM 시나리오를 모두 활용하기에는 많은 시간과 노력이 필요하기 때문에 이를 효율적으로 수행할 수 있는 방법이 필요하다 본 연구에서는 시나리오 저감(scenario reduction)방법을 이용하여, 수많은 GCM 시나리오 대신 몇 개의 대표적 GCM 시나리오로도 충분히 불확실성을 유지할 수 있는 시나리오 저감(scenario reduction)방법을 수립하고 제시하였다. IPCC 기후시나리오 중 20C3M과 A & B 배출시나리오를 바탕으로 생산되는 71개의 GCM 시나리오를 다운로드 받아 월평균 기온과 강수량에 대하여 한반도를 대상으로 분석하였다. 비교결과, 기온 전망은 실측과 비슷한 경향성을 보였으나 강수량은 홍수기를 모의하지 못하는 것으로 나타났다. 시나리오 저감방법은 시나리오 분류(scenario cluster)방법과 시나리오 선정(scenario selection) 방법으로 구성된다. 시나리오 분류방법에서는 k-mean방법을 이용하여 5개의 cluster로 나누었으며, 시나리오 선정방법에서는 GCM 시나리오 선정기법을 조사 분석하여 연구방향과 목적에 따라 GCM 시나리오 선정기법을 선택할 수 있는 표를 제시하고, 이 중 시나리오의 확률밀도함수를 이용하는 PDF method를 적용하였다. 본 연구에서는 불확실성 정량화를 위해 maximum entropy를 이용하였다. 또한 시나리오 저감방법이 불확실성을 유지하는지 비교하기 위해 PDF method를 이용하여 정확성이 높은 순으로 5개의 GCM 시나리오를 선정(best 시나리오)하여 불확실성을 정량화하였다. GCM 시나리오의 분산을 이용하여 maximum entropy를 산정한 결과, 20C3M 배출시나리오에서는 모든 시나리오의 entropy는 3.08, 시나리오 저감방법은 2.75, best 시나리오는 2.28이었으며, 이는 시나리오 저감방법은 모든 시나리오의 89.3%의 불확실성을 설명하고 있으나 best 시나리오는 74.0%밖에 설명하지 못한다는 것을 나타낸다. A & B 배출시나리오에서도 시나리오 저감 방법을 사용한 GCM 시나리오가 best 시나리오보다 모든 시나리오의 불확실성을 더 잘 설명하는 것으로 나타났다. 이와 같이 수많은 GCM 시나리오를 사용하는 것보다 몇 개의 대표 시나리오를 이용하여 기후 변화 불확실성을 유지하면서 미래전망을 할 수 있다면, 매우 효율적으로 기후변화 연구를 수행할 수 있을 것으로 사료된다.

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.199-206
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• 2006

Sequential pattern mining is an important data mining task with broad applications. However, conventional methods may meet inherent difficulties in mining databases with long sequences and noise. They may generate a huge number of short and trivial patterns but fail to find interesting patterns shared by many sequences. In this paper, to overcome these problems, we propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. The proposed method works in two steps: one is to cluster target sequences by their similarities and the other is to find consensus patterns that ire similar to the sequences in each cluster directly through multiple alignment. For this purpose, a novel structure called weighted sequence is presented to compress the alignment result, and the longest consensus pattern that represents each cluster is generated from its weighted sequence. Finally, the effectiveness of the proposed method is verified by a set of experiments.

• Journal of the Korean Society of Clothing and Textiles
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• v.20 no.2
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• pp.282-297
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• 1996

The effective construction for ready-made clothes is one of the central concerns of both consumers and manufactuers in today's apparel industry. In order to reduce the burden of stocks and increase clothing fitness, systematic information on typical body sizes and somatotypes is essential. The purpose of this study i-: to provide basic data on young women's somatotypes for form designers and pattern makers. The subjects of the survey were 310 women of 18 to 26 years old. The study collected 84 anthropometric data for each Person. The data was analyzed by using of the multivariate method. The factor analysis was utilized in regard to the 65 items obtained from anthropometric measurement respectively. The principal component analysis was applied to the data with orthogonal rotation after extraction. The factor scores used in the factor analysis became the basis of determining the value of each variable of the cluster analysis. The cluster analysis was applied for identifying typical somatotypes. Ward's minimum variance method was applied for the purpose of extracting distance metrix by the standardized Euclidean distance. The element forming each cluster can be subdivided into several sets by crosstabulation which is obtained by the fastclus of the SAS. This research has demonstrated 3 distinctive types of silhouette contour of the trunk. Incidentally it also identified 4 of the lower body from the waistline to thigh contour respectively. The discriminant analysis showed that the most significant discriminant factor of the trunk classification were side neck point -1 scapular -1 waistiline length and waist girth. In Korea, the average somatotype of female college students tends to be tall, slim and straight. Reviewing the relationship between the classifications of three parts of body, they are related to each other to some extent but their distribution are not constant. Therefore, in view of clothing construction, a proper separation of the body surface is a necessity.

• Journal of the Korean Geographical Society
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• v.44 no.5
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• pp.605-623
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• 2009

This study is aimed to classify rainfall regions in Pakistan. Classification of rainfall regions is essential to understand rainfall patterns in Pakistan. Rainfall patterns have been investigated using a factor and cluster analysis technique by 10-days rainfall parameter. The data used here have been obtained from 32 specific weather stations of PMD (Pakistan Meteorological Department) for the period of January 1980 to December 2006. The results obtained from factor analysis provide three factors and these three factors accounts for 94.60% of the total variance. For a better understanding of rainfall regions, cluster analysis method has been applied. The clustering procedure is based on the Wards method algorithm. Overall, these rainfall regions have been divided into six groups. The boundary of the region is determined by the topology such as Baluchistan plateau, Indus plain, Hindu Kush and Himalaya ranges.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.129-139
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• 2003

The PC cluster architecture is considered as a cost-effective alternative to the existing supercomputers for realizing a high-performance information retrieval (IR) system. To implement an efficient IR system on a PC cluster, it is essential to achieve maximum parallelism by having the data appropriately distributed to the local hard disks of the PCs in such a way that the disk I/O and the subsequent computation are distributed as evenly as possible to all the PCs. If the terms in the inverted index file can be classified to closely related clusters, the parallelism can be maximized by distributing them to the PCs in an interleaved manner. One of the goals of this research is the development of methods for automatically clustering the terms based on the likelihood of the terms' co-occurrence in the same query. Also, in this paper, we propose a method for duplicate distribution of inverted index records among the PCs to achieve fault-tolerance as well as dynamic load balancing. Experiments with a large corpus revealed the efficiency and effectiveness of our method.

• Journal of Forest and Environmental Science
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• v.29 no.3
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• pp.219-225
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• 2013

The correlation between disturbance intensity and stand development was evaluated on the basis of natural forest community type classification in areas of Baekhaksan (more disturbed area) and Hwangaksan (less disturbed area). The vegetation data were collected by point-centered quarter sampling method, and they were subjected to cluster analysis for classifying community types and to analysis of species composition and species diversity for reviewing ecological characteristics. By the method of cluster analysis, natural forests of Baekhaksan were classified into Quercus forest community, Pinus densiflora community, Q. variabilis community, and Q. mongolica community. Those of Hwangaksan were divided into P. densiflora community, Q. mongolica community, Q. forest community, and Mixed mesophytic community. It is presumed that more developed and less disturbed forest area shows multiple species community pattern and more diversified structure than less developed and more disturbed forest area. In Korean peninsula, the abundance of red pine may play an important indicator to estimate the developmental pattern of the forest after artificial disturbance. The overall evaluation indicated that less disturbed Hwangaksan area had more developed stand pattern with mixed mesophytic community, more complicated species composition, and higher species diversity than Baekhaksan area.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.39-51
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• 2012

Cluster analysis has proven to be a useful tool for investigating the association structure among genes and samples in a microarray data set. We applied several cluster validation measures to evaluate the performance of clustering algorithms for analyzing microarray gene expression data, including hierarchical clustering, K-means, PAM, SOM and model-based clustering. The available validation measures fall into the three general categories of internal, stability and biological. The performance of clustering algorithms is evaluated using simulated and SRBCT microarray data. Our results from simulated data show that nearly every methods have good results with same result as the number of classes in the original data. For the SRBCT data the best choice for the number of clusters is less clear than the simulated data. It appeared that PAM, SOM, model-based method showed similar results to simulated data under Silhouette with of internal measure as well as PAM and model-based method under biological measure, while model-based clustering has the best value of stability measure.

• Journal of Korean Academy of Nursing Administration
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• v.9 no.3
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• pp.415-427
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• 2003

Purpose: The purpose of this study was to investigate the degree and pattern of knowledge management process, and to identify the relationship between knowledge management process and organizational effectiveness in clinical nurses. Method: Participants were 665 regular clinical nurses who had worked for over 1 year in general units of 9 tertiary medical hospitals including 2 national university hospitals, 5 university hospitals, and 2 hospitals founded by business enterprises. Data were collected from March to May 2003 through questionnaires. Four structured instruments were used to collect the data: Knowledge Management Process Scale(Jeong, Lee, Lee, & Kim, 2003), cCommitment Questionnaire(Mowday, Steers, & Porter, 1979), General Satisfaction Scale(CooK, Hepworth, Wall, & Warr, 1981), and one for general characteristics. The data were analyzed using factor analysis, reliability analysis, descriptive analysis, cluster analysis, one-way ANOVA, Scheffe test, correlation analysis with the SPSS for Windows 10.0 program. Result: 1) The average score for knowledge management process in nurses was $3.08{\pm}.54$ on a 5-point Likert scale. In order from highest mean score, the elements of knowledge management process, were Knowledge $Utilization(3.35{\pm}.57)$, Knowledge $Sharing(3.07{\pm}.58)$, Knowledge $Creation(2.99{\pm}.63)$, and Knowledge $Storage(2.91{\pm}.82)$. 2) Four knowledge management patterns for nurses, which were derived from cluster analysis, were inactivate pattern, delayed pattern, activate pattern, and high-activate pattern of knowledge management. 3) The degree of knowledge management process activation and 4 elements of knowledge management process, Knowledge Creation, Knowledge Storage, Knowledge Sharing, and Knowledge Utilization, were significantly correlated with nurses' organizational commitment and job satisfaction(p=.000). 4) The nurses' organizational commitment and job satisfaction showed significant differences according to the knowledge management patterns derived from cluster analysis of high-activate pattern, activate pattern, delayed pattern, inactivate pattern(p=.000). Conclusion: These results suggest that there are four knowledge management patterns for nurses, and knowledge management process positively affects the nurses' organizational commitment and job satisfaction. From the above findings, knowledge management process is empirically verified as a useful and effective method to increase organizational effectiveness, and develop the organization.

• Bulletin of the Korean Chemical Society
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• v.31 no.4
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• pp.934-940
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• 2010

The reduction of [$Os_3(CO)_{11}P(OMe)_3$] and subsequent ionic coupling of the reduced species with $[Ru({\eta}^5-C_5H_5)(CH_3CN)_3]^+$ resulted in the formation of [$Os_3(CO)_{11}P(OMe)_3(Ru({\eta}^5-C_5H_5))_2$] which can be converted to spiked tetrahedral cluster, [$HOs_3(CO)_{11}P(OMe)_3Ru_2({\eta}^5-C_5H_5)(C_5H_4)$] via the intramolecular hydrogen transfer. Due to the unavailability of a suitable single crystal, the PW91/SDD and LDA/SDD density functional methods were used to predict possible structures and the available spectroscopic information (IR, NMR) of [$Os_3(CO)_{11}P(OMe)_3(Ru({\eta}^5-C_5H_5))_2$]. The most probable geometry found by constrained search is the isomer (a2) in which the phosphite, $P(OMe)_3$, occupies an axial position on one of the two osmium atoms that is edge bridged by the $Ru(CO)_2({\eta}^5-C_5H_5)$ unit. By using the most probably geometry, the predicted infrared frequencies and $^1H$, $^{13}C$ and $^{31}P$ NMR chemical shifts of the compound are in the same range as the experimental values. For this type of complex, the LDA/SDD method is appropriate for IR predictions whereas the OPBE/IGLO-II method is appropriate for NMR predictions. The activation energy and reaction energy of the intramolecular hydrogen transfer coupled with the structural change of the transition metal framework were estimated at the PW91/SDD level to be 110.32 and -0.14 kcal/mol respectively.