• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.265-276
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• 2015

In this paper, we propose hierarchical cluster analysis and multidimensional scaling for joint distribution valued data. Information technology is increasing the necessity of statistical methods for large and complex data. Symbolic Data Analysis (SDA) is an attractive framework for the data. In SDA, target objects are typically represented by aggregated data. Most methods on SDA deal with objects represented as intervals and histograms. However, those methods cannot consider information among variables including correlation. In addition, objects represented as a joint distribution can contain information among variables. Therefore, we focus on methods for joint distribution valued data. We expanded the two well-known exploratory methods using the dissimilarities adopted Hall Type relative projection index among joint distribution valued data. We show a simulation study and an actual example of proposed methods.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.965-974
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• 2003

In this paper we propose an incremental classification of multi-class data set by LS-SVM. By encoding the output variable in the training data set appropriately, we obtain a new specific output vectors for the training data sets. Then, online LS-SVM is applied on each newly encoded output vectors. Proposed method will enable the computation cost to be reduced and the training to be performed incrementally. With the incremental formulation of an inverse matrix, the current information and new input data are used for building another new inverse matrix for the estimation of the optimal bias and lagrange multipliers. Computational difficulties of large scale matrix inversion can be avoided. Performance of proposed method are shown via numerical studies and compared with artificial neural network.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.491-503
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• 2002

In this paper, we propose a automatic document summarization method based on Principal Component Analysis(PCA) which is one of the multivariate statistical methods. After extracting thematic words using PCA, we select the statements containing the respective extracted thematic words, and make the document summary with them. Experimental results using newspaper articles show that the proposed method is superior to the method using either word frequency or information retrieval thesaurus.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.457-469
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• 2003

In this paper, we have proposed the efficient algorithm that can segment the video scene change using a various statistical characteristics obtained from by applying the wavelet transformation for each frames. Our method firstly extracts the histogram features from low frequency subband of wavelet-transformed image and then uses these features to detect the abrupt scene change. Second, it extracts the edge information from applying the mesh method to the high frequency subband of transformed image. We quantify the extracted edge information as the values of variance characteristic of each pixel and use these values to detect the gradual scene change. And we have also proposed an algorithm how extract the proper key frame from segmented video scene. Experiment results show that the proposed method is both very efficient algorithm in segmenting video frames and also is to become the appropriate key frame extraction method.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.879-890
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• 2007

Statistical matching is a method of data integration for data sources that do not share the same units. It could produce rapidly lots of new information at low cost and decrease the response burden affecting the quality of data. This paper proposes a statistical matching technique combining k-NN (k-nearest neighborhood) and regression methods. We select k records in a donor file that have similarity in value with a specific observation of the common variable in a recipient file and estimate an imputation value for the recipient file, using regression modeling in the donor file. An empirical comparison study is conducted to show the properties of the proposed method.

• Korean Journal of Oriental Medicine
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• v.13 no.3
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• pp.45-51
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• 2007

This Study is to propose the method about statistical factor from the old oriental medical record in Korea. This Study reviews the statistical analysis recently published in the old oriental medical books which are in the Research Report by the Korea Institute of Oriental Medicine at 2007. The results reveal a disease factors, prescription factors.medicines factors, population factors and historical factors by the statistics. The results show that the old oriental medical record for a information system need the interpreting with a information analysts, a statistics analysts and an oriental medical doctors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.11 no.4
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• pp.268-279
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• 1986

Recently, the improtance of the ISDN has been emphasized for the new services in the future information society. In this paper, new application of statistical multiplexer which can be used for CCITT D-channel multiplexing is suggested. The basic architecture of statistical multiplexer which can interleave eleven 16Kbps D-channels into one 64Kbps B-channel is also proposed. The necessary buffer size was estimated by computer simulation considering data traffic intensity, signal arrival length distribution and buffer overflow probability. Based on this buffer size, the basic architecture of the statistical multiplexer has been survetyed and hardware design principles are also studied.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.1
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• pp.109-114
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• 2010

In this paper, we developed the web content of statistical analysis using statistical package and Active Server Page (ASP). A statistical package is very difficult to learn and use for non-statisticians, however, non-statisticians want to do analyze the data without learning statistical packages such as SAS, S-plus, and R. Therefore, we developed the web based statistical analysis contents using S-plus which is the popular statistical package and ASP. In real application, we developed the web content for various statistical analyses such as exploratory data analysis, analysis of variance, and time series on the web using water quality data. The developed statistical analysis web content is very useful for non-statisticians such as public service person and researcher. Consequently, combining a web based contents with a statistical package, the users can access the site quickly and analyze data easily.

• Journal of the Korean Statistical Society
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• v.36 no.3
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• pp.435-445
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• 2007

This paper addresses for the first time the use of complete data information criteria in unsupervised learning of the Naive-Bayes model. A Monte Carlo study sets a large experimental design to assess these criteria, unusual in the Bayesian network literature. The simulation results show that complete data information criteria underperforms the Bayesian information criterion (BIC) for these Bayesian networks.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.655-665
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• 2000

Several information criterions, Schwarz information criterion (SIC), Akaike information criterion (AIC), and the modified Akaike information criterion ($AIC_c$), are proposed to locate a change point in the multiple linear regression model. These methods are applied to a stock Exchange data set and compared to the results.