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		<title>RSS 게시판 2.0</title>
		<link>/usr/local/tomcat/webapps/ROOT/sites/yschoi/</link>
		<description><![CDATA[This is RSS Board.]]></description>
		<language>ko</language>
		
		
			
			
			
				
					
						
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								<title><![CDATA[Review of Basic Concepts in Multivariate Data,}]]></title>
								<link>/bbs/yschoi/2541/1457277/artclView.do?layout=unknown</link>
								<pubDate>2025-08-20 11:35:55.203</pubDate>
								<author>최용석</author>
								<description><![CDATA[ ]]></description>
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								<title><![CDATA[Additional References(2025)}]]></title>
								<link>/bbs/yschoi/2541/1457276/artclView.do?layout=unknown</link>
								<pubDate>2025-07-28 10:57:08.753</pubDate>
								<author>최용석</author>
								<description><![CDATA[ ]]></description>
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								<title><![CDATA[A.Machine Learnings(ML)}]]></title>
								<link>/bbs/yschoi/2541/1457275/artclView.do?layout=unknown</link>
								<pubDate>2025-07-28 10:55:09.91</pubDate>
								<author>최용석</author>
								<description><![CDATA[ ]]></description>
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								<title><![CDATA[8. Correspondence Analysis(CRA)}]]></title>
								<link>/bbs/yschoi/2541/1457274/artclView.do?layout=unknown</link>
								<pubDate>2025-07-28 10:54:03.047</pubDate>
								<author>최용석</author>
								<description><![CDATA[ ]]></description>
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								<title><![CDATA[7.Multidimensional Scaling(MDS)}]]></title>
								<link>/bbs/yschoi/2541/1457273/artclView.do?layout=unknown</link>
								<pubDate>2025-07-28 10:53:04.84</pubDate>
								<author>최용석</author>
								<description><![CDATA[ ]]></description>
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								<title><![CDATA[6. Discriminant Analysis(DCA)}]]></title>
								<link>/bbs/yschoi/2541/1457272/artclView.do?layout=unknown</link>
								<pubDate>2025-07-28 10:51:44.793</pubDate>
								<author>최용석</author>
								<description><![CDATA[ ]]></description>
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								<title><![CDATA[Welcome and Introduction}]]></title>
								<link>/bbs/yschoi/2541/1457271/artclView.do?layout=unknown</link>
								<pubDate>2025-07-28 10:50:32.07</pubDate>
								<author>최용석</author>
								<description><![CDATA[ ]]></description>
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								<title><![CDATA[Additional Lecture Notes}]]></title>
								<link>/bbs/yschoi/2541/1457270/artclView.do?layout=unknown</link>
								<pubDate>2023-08-23 14:57:35.39</pubDate>
								<author>최용석</author>
								<description><![CDATA[1) Clustering in Text Mining2) Applications of  Multiple and Simple CRA inText Mining3) Application ofHierarchical CA, MDS, SVM and DNN in Text Mining]]></description>
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								<title><![CDATA[최용석(2018). Data Scientist로 살아가는 Big Data 시대의 산책}]]></title>
								<link>/bbs/yschoi/2541/1457269/artclView.do?layout=unknown</link>
								<pubDate>2020-11-12 09:49:39.793</pubDate>
								<author>최용석</author>
								<description><![CDATA[ ]]></description>
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								<title><![CDATA[최순국(2011). Social Network Analysis and It’s Applications for Co-authors and Key-words Networks in the JKSS}]]></title>
								<link>/bbs/yschoi/2541/1457268/artclView.do?layout=unknown</link>
								<pubDate>2020-11-12 09:44:34.88</pubDate>
								<author>최용석</author>
								<description><![CDATA[Abstract Social network analysis is a graphical technique to search relationships and characteristics between nodes(people, companies and organs, etc) and an important node positioning in the visualized social network figure.But it is dicult to characterize nodes in this figure. Therefore, their rel]]></description>
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								<title><![CDATA[최요셉(2014). Standardizing Unstructured Big Data and Visual Interpretation using MapReduce and Correspondence Analysis}]]></title>
								<link>/bbs/yschoi/2541/1457267/artclView.do?layout=unknown</link>
								<pubDate>2020-11-12 09:39:13.143</pubDate>
								<author>최용석</author>
								<description><![CDATA[The Korean Journal of Applied Statistics (2014) 27(2), 169{183  AbstractMassive and various types of data recorded everywhere are called big data. Therefore, it is important toanalyze big data and to nd valuable information. Besides, to standardize unstructured big data is importantfor the applicati]]></description>
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								<title><![CDATA[정호영(2018). Comparison of term weighting techniques for text classification}]]></title>
								<link>/bbs/yschoi/2541/1457266/artclView.do?layout=unknown</link>
								<pubDate>2020-11-12 09:34:47.44</pubDate>
								<author>최용석</author>
								<description><![CDATA[  AbstractThe document-term frequency matrix is a general data of x-objects in text mining. In this study, we introduce a traditional term weighting scheme TF-IDF (term frequency-inverse document frequency) which is applied in the document-term frequency matrix and used for text classifications. In ]]></description>
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								<title><![CDATA[이수진(2020). Document classification using a deep neural network(한국통계학회 논문 포스트 및 출간 논문)}]]></title>
								<link>/bbs/yschoi/2541/1457265/artclView.do?layout=unknown</link>
								<pubDate>2020-10-27 09:26:03.68</pubDate>
								<author>최용석</author>
								<description><![CDATA[ Abstract The document-term frequency matrix is a term extracted from the documetns in which the group information exists in text mining. In this study, we generated the document-term frequency matrix for document classification according to research field. We applied the traditional term weighting ]]></description>
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								<title><![CDATA[정민지(2017). Creation and clustering of proximity data for text data analysis(한국통계학회 논문 포스트)}]]></title>
								<link>/bbs/yschoi/2541/1457264/artclView.do?layout=unknown</link>
								<pubDate>2020-10-27 09:10:26.08</pubDate>
								<author>최용석</author>
								<description><![CDATA[AbstractDocument-term frequency matrix is a type of data used in text mining. This matrix is often based on variousdocuments provided by the x-objects to be analyzed. When analyzing x-objects using this matrix, researchersgenerally select only terms that are common in documents belonging to one x-ob]]></description>
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								<title><![CDATA[Reference: ANN(Artificial Neural Network) for Classification}]]></title>
								<link>/bbs/yschoi/2541/1457263/artclView.do?layout=unknown</link>
								<pubDate>2019-11-21 11:38:58.097</pubDate>
								<author>최용석</author>
								<description><![CDATA[Computational model that mimics the pattern of the human mind.     The development of a neural network is inspired by human brain activities.     The neural network is a network made up of artificial neurons (or nodes).      Three types of neurons within the network:               input neurons, hid]]></description>
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								<title><![CDATA[Reference: SVM( Support Vector Machine ) for Classification  }]]></title>
								<link>/bbs/yschoi/2541/1457262/artclView.do?layout=unknown</link>
								<pubDate>2019-11-14 15:04:09.51</pubDate>
								<author>최용석</author>
								<description><![CDATA[SVM( Support Vector Machine) is the Reference for Lecture 7: Discriminant Analysis.- Example 1. Riding Mower Data  with Two Clusters- Example 2. Iris Flowers Data  with Three Clusters   - SVM was developed by Cortes and Vannik(1995) for binary classification.- SVM is a powerful technique for general]]></description>
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								<title><![CDATA[Reference: Chi-square test for Three Ways Table}]]></title>
								<link>/bbs/yschoi/2541/1457261/artclView.do?layout=unknown</link>
								<pubDate>2019-11-12 15:43:42.787</pubDate>
								<author>최용석</author>
								<description><![CDATA[Reference for Lecture 8. Correspondence Analysis    :  Chi-square test for Three Ways Table(Cochran-Mantel-Haenszel(CMH) test) - Example 1. Customer Data with Sex, Age and Height. - Example 2.  Driver Injury Data wtith Condition, Seat belt usuage and Injury level. - Example 3.  Driver Injury Table D]]></description>
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							<item>
								<title><![CDATA[Welcome and Introduction}]]></title>
								<link>/bbs/yschoi/2541/1457260/artclView.do?layout=unknown</link>
								<pubDate>2014-12-24 00:00:00.0</pubDate>
								<author>최용석</author>
								<description><![CDATA[ E-mail: yschoi@pusan.ac.kr  http://yschoi.pusan.ac.kr     - Methodology of InstructionWith practice times, we will be experienced in results&#039; interpretations and visualizations of multivariate data analysis using the R.  - Course x-x-x-x-x-x-objectives and Description In Multivariate Statistics (II]]></description>
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								<title><![CDATA[Lecture 6. Discrimination and Classification Analysis (DCA)}]]></title>
								<link>/bbs/yschoi/2541/1457259/artclView.do?layout=unknown</link>
								<pubDate>2014-12-24 00:00:00.0</pubDate>
								<author>최용석</author>
								<description><![CDATA[Lecture 6. Discrimination and Classification Analysis(DCA)  6.1 Understanding DCA6.2 DCA with Two Clusters6.3 DCA of Two MultivariateNormal Clusters6.4 DCA with Several Clusters6.5 DCA of Several Multivariate Normal Clusters 6.6 Evaluation of DCA6.7Fisher’s LDA with Two Clusters6.8Fisher’sLDA with S]]></description>
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								<title><![CDATA[Lecture 7. Multidimensional Scaling (MDS)}]]></title>
								<link>/bbs/yschoi/2541/1457258/artclView.do?layout=unknown</link>
								<pubDate>2014-12-24 00:00:00.0</pubDate>
								<author>최용석</author>
								<description><![CDATA[Lecture 7. Multidimensional Scaling (MDS)    7.1 Comprehension of MDS 7.2 Metric MDS 7.3 Non-Metric MDS   7.4 Three-way matrix MDS 7.5 Nonlinear MDS  7.6 R for MDS : Practice Time     ]]></description>
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								<title><![CDATA[Lecture 8. Correspondence Analysis (CRA)}]]></title>
								<link>/bbs/yschoi/2541/1457257/artclView.do?layout=unknown</link>
								<pubDate>2014-12-24 00:00:00.0</pubDate>
								<author>최용석</author>
								<description><![CDATA[Lecture 8. Correspondence Analysis (CRA )   8.1 Comprehension of CRA 8.2 Simple CRA 8.3 Independence  Homogeneity in CRA  8.4 MultipleCRA 8.5 MCRA of Classification VariablesData  8.6 R for CRA : Practice Time    ]]></description>
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								<title><![CDATA[Lecture A. Machine Learnings}]]></title>
								<link>/bbs/yschoi/2541/1457256/artclView.do?layout=unknown</link>
								<pubDate>2014-12-24 00:00:00.0</pubDate>
								<author>최용석</author>
								<description><![CDATA[A. Machine Learnings (ML)A.1 Comprehension of MLA.2 SVM(support vector machine) A.3 ANN(Artificial Neural Network)A.4 R for ML : Practice Time ]]></description>
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