• Journal of the Korean Institute of Landscape Architecture
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• v.33 no.1 s.108
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• pp.109-120
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• 2005

This landscape design proposal was presented to a design competition for Osong Biohealth Technopolis Institute of Cheongwon Gun Chung Cheong Buk Do which was held by Ministry of Health and Welfare in March 2004. The site is located in. Osong Li, Kang Wei Myun, Cheonwon Gun, Chung Cheong Buk Do and has an area of $402,600m^2$. The judging criteria for landscape design set by the client could be articulated as follows: an environment friendly design respecting the surrounding environment, a functionally efficient site plan by clustering buildings with similar uses, a site plan having 'front yard' by locating buildings in rear areas toward existing 'groves'. The proposal set the main design concept of this project as 'clustering'. By doing that, existing grades and plants can be saved, buildings with similar uses can be clustered, huge 'front yard' as a symbolic image of this project can be achieved, and finally many small open spaces for everyday life can be designed accordingly.

• The Journal of Asian Finance, Economics and Business
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• v.1 no.2
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• pp.31-37
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• 2014

The portfolio selection is one of the most important and vital decisions that a real or legal person, who invests in stock market, should make. The main purpose of this article is the determination of the optimal portfolio with regard to relations among stock returns of companies which are active in Tehran's stock market. For achieving this goal, weekly statistics of company's stocks since Farvardin 1389 until Esfand 1390, has been used. For analyzing statistics and information and examination of stocks of companies which has change in returns, factors analysis approach and clustering analysis has been used (FC approach). With using multivariate analysis and with the aim of reducing the unsystematic risk, a financial portfoliois formed. At last but not least, results of choosing the optimal portfolio rather than randomly choosing a portfolio are given.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.885-886
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• 2008

This paper presents a reference template design method for frame-based classification of underwater transient signals. In the proposed method, framebased feature vectors of each reference signal are clustered by using LBG clustering algorithm to reduce the number of feature vectors in each class. Experimental results have shown that drastic reduction of the reference database can be achieved while maintaining the classification performance with LBG clustering algorithm.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.4
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• pp.31-37
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• 2003

This paper is a review of the large-scale information retrieval system, CONDOR. This system was developed by the consortium that consists of Chonbuk National University, Searchline Co. and Carnegie Mellon University. This system is based on the probabilistic model of information retrieval systems. The multi-language query processing, online document summarization based on query and dynamic hierarchy clustering of this system make difference of other systems. We test this system with 30 million web documents successfully.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.925-937
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• 2008

In this study, we considered the clustering analysis for stock return traded in the stock market. Most of financial time-series data, for instance, stock price and exchange rate have conditional heterogeneous variability depending on time, and, hence, are not properly applied to the autoregressive moving-average(ARMA) model with assumption of constant variance. Moreover, the variability is font and center for stock investors as well as academic researchers. So, this paper focuses on the generalized autoregressive conditional heteroscedastic(GARCH) model which is known as a solution for capturing the conditional variance(or volatility). We define the metrics for similarity of unconditional volatility and for homogeneity of model structure, and, then, evaluate the performances of the metrics. In real application, we do clustering analysis in terms of volatility and structure with stock return of the 11 Korean companies measured for the latest three years.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.664-677
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• 2011

In order to control interference and improve spectrum efficiency in the femtocell and macrocell overlaid system (FMOS), we propose a joint frequency bandwidth dynamic division, clustering and power control algorithm (JFCPA) for orthogonal-frequency-division-multiple access-based downlink FMOS. The overall system bandwidth is divided into three bands, and the macro-cellular coverage is divided into two areas according to the intensity of the interference from the macro base station to the femtocells, which are dynamically determined by using the JFCPA. A cluster is taken as the unit for frequency reuse among femtocells. We map the problem of clustering to the MAX k-CUT problem with the aim of eliminating the inter-femtocell collision interference, which is solved by a graph-based heuristic algorithm. Frequency bandwidth sharing or splitting between the femtocell tier and the macrocell tier is determined by a step-migration-algorithm-based power control. Simulations conducted to demonstrate the effectiveness of our proposed algorithm showed the frequency-reuse probability of the FMOS reuse band above 97.6% and at least 70% of the frequency bandwidth available for the macrocell tier, which means that the co-tier and the cross-tier interference were effectively controlled. Thus, high spectrum efficiency was achieved. The simulation results also clarified that the planning of frequency resource allocation in FMOS should take into account both the spatial density of femtocells and the interference suffered by them. Statistical results from our simulations also provide guidelines for actual FMOS planning.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.1
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• pp.295-318
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• 2014

The purpose of this study is to analyze the intellectual structure of reading studies by using Co-Word Analysis based on the mixed weight in which the level of academic journals and the position of keywords are calculated. To achieve it, 838 academic articles relating to reading studies from KCI during the period from 2003 to 2012 were retrieved and 56 keywords were extracted. The results of clustering analysis, MDS, network analysis are that the network based on the mixed weight has a better performance in above three methods and reading studies can be divided into 4 bigger divisions and 11 subdivisions. Finally, the result of document analysis shows reading studies changes its research tendency from theoretical studies to empirical studies.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.101-126
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• 2017

Data Science is emerging as a closely related field of study to Library and Information Science (LIS), and as an interdisciplinary subject combining LIS, statistics and computer science in an attempt to understand the value of data by applying what LIS has been doing for collecting, storing, organizing, analyzing, and utilizing information. To investigate which subject fields other than LIS, statistics, and computer science are related to Data Science, this study retrieved 667 materials from Web of Science Core Collection, extracted terms representing Web of Science Categories, examined subject fields that are studying Data Science using descriptive analysis, analyzed the intellectual structure of the field by co-word analysis and network analysis, and visualized the results as a Pathfinder network with clustering created with the PNNC clustering algorithm. The result of this study might help to understand the intellectual structure of the Data Science field, and may be helpful to give an idea for developing relatively new curriculum.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3836-3854
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• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.