• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1207-1212
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• 2009

Basic objective in cluster analysis is to discover natural groupings of items or variables. In general, clustering is conducted based on some similarity (or dissimilarity) matrix or the original input text data. Various measures of similarities (or dissimilarities) between objects (or variables) are developed. We introduce a real application problem of clustering procedure in SPSS when the distance matrix of the objects (or variables) is only given as an input data. It will be very helpful for the cluster analysis of huge data set which leads the size of the proximity matrix greater than 1000, particularly. Syntax command for matrix input data in SPSS for clustering is given with numerical examples.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.9
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• pp.423-428
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• 2006

In this paper we propose a new watermarking scheme using a data matrix and a cryptograph key. The data matrix of two-dimensional bar codes is a new technology capable of holding relatively large amounts of data compared to the conventional one-dimensional bar code. And a cryptograph key is used to prevent a watermark from malicious attacks. We encoded the copyright information into a data matrix bar code, and it was spread as a pseudo random pattern using the owner key. The experimental results show that the proposed scheme has good quality and is robust to various attacks, such as JPEG compression, filtering and resizing. Also the performance of the proposed scheme is verified by comparing the copyright information with the information which is extracted from the watermark.

• Journal of the Korean Mathematical Society
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• v.33 no.4
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• pp.717-734
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• 1996

For a scalar rational function, the spectral data consisting of zeros and poles with their respective multiplicities uniquely determines the function up to a nonzero multiplicative factor. But due to the richness of the spectral structure of a rational matrix function, reconstruction of a rational matrix function from a given spectral data is not that simple.

• Journal of the Optical Society of Korea
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• v.16 no.4
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• pp.443-448
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• 2012

A polarimetric experimental method was developed to determine the Jones matrix elements of transparent optical materials without sign ambiguity. A set of polarization dependent transmittance data of the samples was measured with polarizer - sample - analyzer system and another set of data was measured with polarizer - sample - quarter-wave plate - analyzer. Two data sets were compared and mathematically analyzed to obtain the correct signs of the elements of the matrix. The Jones matrix elements of a quarter-wave plate were determined to check the validity of the method. The experimentally obtained matrix elements of the quarter-wave plate were consistent with the theoretical expectations. The same method was applied to obtain the Jones matrix elements of a twisted nematic liquid crystal panel.

• Journal of Microbiology
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• v.33 no.2
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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.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.727-733
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• 2011

To understand the stock market structure it is very important to extract meaningful information by analyzing the correlation matrix between stock returns. Recently there has been many studies on the correlation matrix using the Random Matrix Theory. In this paper we adopt this random matrix methodology to a single-factor model and we obtain meaningful information on the correlation matrix. In particular we observe the analysis of the correlation matrix using the single-factor model explains the real market data and as a result we confirm the usefulness of the single-factor model.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1795-1796
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• 2007

본 논문은 DWT(Discrete Wavelet Transform)기반의 블라인드 워터마킹 기법을 제안 하였다. DWT를 하였을 때, 두 개의 서브밴드들의 계수 값을 변환하여 워터마크를 삽입한다. 기존에는 워터마크를 로고나 signature등을 많이 사용 하였으나, 여기서는 이차원 바코드인 Data Matrix를 워터마크로 사용 하였다. Data Matrix자체가 오류 검출 및 복원 알고리즘을 가지고 있어, 워터마크 추출 시 비교적 작은 에러는 Data Matrix의 복원 알고리즘에 의해 Data Matrix의 암호화된 정보를 복원 할 수 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.205-211
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• 2012

The retrieved documents have to be transformed into proper data structure for the clustering algorithms of statistics and machine learning. A popular data structure for document clustering is document-term matrix. This matrix has the occurred frequency value of a term in each document. There is a sparsity problem in this matrix because most frequencies of the matrix are 0 values. This problem affects the clustering performance. The sparseness of document-term matrix decreases the performance of clustering result. So, this research uses the factor score by factor analysis to solve the sparsity problem in document clustering. The document-term matrix is transformed to document-factor score matrix using factor scores in this paper. Also, the document-factor score matrix is used as input data for document clustering. To compare the clustering performances between document-term matrix and document-factor score matrix, this research applies two typed matrices to self organizing map (SOM) clustering.

• Convergence Security Journal
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• v.16 no.4
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• pp.3-15
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• 2016

Business data means data of all the documents and electronically generated on / off-line form, storage, use, and transfer the company work process. Business, organization, sales, marketing, means any data related to shipping. Many companies are investing in privacy. But not so for business data. In most companies, secret, confidential rating already exists, the basis is insufficient to establish that decisions can be analyzed in detail to reflect the actual business data in use. In this paper we want to present the criteria that can offer ways to design your business data decision matrix to establish the qualitative and quantitative criteria (evaluation indicators) that can be classified business data and protected by each class.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.1007-1012
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• 2003

A recursive procedure for finding the Cholesky root of the inverse of sample covariance matrix, leading to a direct solution for the inverse of a positive definite matrix, is developed using the likelihood equation for the maximum likelihood estimation of the Cholesky root under normality assumptions. An example of the Hilbert matrix is considered for an illustration of the procedure.