• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.8
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• pp.1998-2004
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• 2009

Recently, due to technical developments of various storage devices and networks, the amount of data increases rapidly. The large volume of data streams poses unique space and time constraints on the data mining process. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Most of the researches based on the support are concerned with the frequent itemsets, but ignore the infrequent itemsets even if it is crucial. In this paper, we propose an efficient method WSFI-Mine(Weighted Support Frequent Itemsets Mine) to mine all frequent itemsets by one scan from the data stream. This method can discover the closed frequent itemsets using DCT(Data Stream Closed Pattern Tree). We compare the performance of our algorithm with DSM-FI and THUI-Mine, under different minimum supports. As results show that WSFI-Mine not only run significant faster, but also consume less memory.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.2
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• pp.23-33
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• 2004

Because of a rapid growth of internet environment, it is also fast increasing to exchange message using e-mail. But, despite the convenience of e-mail, it is rising a currently bi9 issue to waste their time and cost due to the spam mail in an individual or enterprise. Many kinds of solutions have been studied to solve harmful effects of spam mail. Such typical methods are as follows; pattern matching using the keyword with representative method and method using the probability like Naive Bayesian. In this paper, we propose a classification method of spam mails from normal mails using Support Vector Machine, which has excellent performance in pattern classification problems, to compensate for the problems of existing research. Especially, the proposed method practices efficiently a teaming procedure with a word dictionary including a generated index by the n-Gram. In the conclusion, we verified the proposed method through the accuracy comparison of spm mail separation between an existing research and proposed scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.549-554
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• 2008

Support Vector machine is the classifier which is based on the statistical training theory. Twin Support Vector Machine(TWSVM) is a kind of binary classifier that determines two nonparallel planes by solving two related SVM-type problems. The training time of TWSVM is shorter than that of SVM, but TWSVM doesn't shows worse performance than that of SVM. This paper proposes the TWSVM which is applied fuzzy membership, and compares the performance of this classifier with the other classifiers using Ionosphere radar data set.

• Korean Journal of Human Ecology
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• v.18 no.2
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• pp.397-406
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• 2009

A Study on the Development of Brassiere Pattern for Elderly Women The purpose of this study is to develop functional and sensible brassiere for elderly women. For this purpose, 6 elderly women(aged between 65 and 69) were sampled to be measured for their body sizes and tested for their bressiere wearing. he results were as follows; 1. In order to develop the basic patterns of elderly women's bressieres, 90B size was selected and then, their bressiere fitting was tested. The foundation pattern of the size 90A was proved for the body by the wearing tests. Each angle and length of the parts on the basic line of the breasts played an important role on setting the pattern. 2. As based on the body surface shell extracted from a plastic mold(photo.2), the cup of brassiere pattern were applied to the body surface shell(photo.4), and full side stretch-wings were applied to 13% reduced body size. The design pattern of elderly women's brassiers were characterized by a round wired as well as a full cup(3piece) embracing the entire breasts. 3. As the result of the wearing test, the excellence of the experimental brassiere was recognized objectively, with high marks in all the items(fig. 6). The experimental brassiere was covering the whole breasts to cope with change according to breasts loosing. It was designed to support the breasts firmly.

• The Journal of the Korea Contents Association
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• v.14 no.10
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• pp.829-839
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• 2014

This study aims at investigating Korean EFL students' writing strategies through their rhetorical patterns and meaning-making for a writing task in an English writing classroom at a Korean university. The participants were the students and teacher in the course, and the data comprised nine pieces of students' opinion writing and interviews with the teacher. To analyze the data, a 'Claim-Support' pattern was adopted. The findings show that most students, 89%, demonstrated the same or similar elements in the 'Claim-Support' pattern for their textual structures and many parts of the meaning-making in their writing were originated from the textbook. These findings reflect that the students pursued the strategy of 'accommodation' in order to succeed in their academic writing regardless of the teacher's intention which focused on creativity and imagination in writing. The conclusion suggest that the students tend to establish their own ways of strategy to cope with the recontextualized setting for writing in English.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.139-146
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• 2014

This paper proposes an efficient method for automatically distinguishing various facial expressions. To recognize the emotions from facial expressions, the facial images are obtained by digital cameras, and a number of feature points were extracted. The extracted feature points are then transformed to 49-dimensional feature vectors which are robust to scale and translational variations, and the facial emotions are recognized by statistical pattern classifiers such Naive Bayes, MLP (multi-layer perceptron), and SVM (support vector machine). Based on the experimental results with 5-fold cross validation, SVM was the best among the classifiers, whose performance was obtained by 50.8% for 6 emotion classification, and 78.0% for 3 emotions.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.1
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• pp.1-8
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• 2004

According to the development of the Internet and the pervasion of Data Base, it is not easy to search for necessary information from the huge amounts of data. In order to do efficient analysis of a large amounts of data, this paper proposes a method for pattern classification based on the effective strategy for dimension reduction for narrowing down the whole data to what users wants to search for. To analyze data effectively, Radial Basis Function Networks based on VC-dimension of Support Vector Machine, a model of statistical teaming, is proposed in this paper. The model of Radial Basis Function Networks currently used performed the preprocessing of Perceptron model whereas the model proposed in this paper, performing independent analysis on VD-dimension, classifies each datum putting precise labels on it. The comparison and estimation of various models by using Machine Learning Data shows that the model proposed in this paper proves to be more efficient than various sorts of algorithm previously used.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.52-56
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• 2004

The Information of classification and estimate about KOSPI200 index`s up and down in the stock market becomes an important standard of decision-making in designing portofolio in futures and option market. Because the coming trend of time series patterns, an economic indicator, is very subordinate to the most recent economic pattern, it is necessary to study the recent patterns most preferentially. This paper compares classification and estimated performance of SVM(Support Vector Machine) and Fuzzy SVM model that are getting into the spotlight in time series analyses, neural net models and various fields. Specially, it proves that Fuzzy SVM is superior by presenting the most suitable dimension to fuzzy membership function that has time series attribute in accordance with learning Data Base.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.774-779
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• 2012

Brain-computer interface based on motor imagery is a system that transforms a subject's intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject's limbs. For the new paradigm, we do not know which positions are activated or not. A simple approach is to use as many channels as possible. The problem is that using many channels causes other problems. When applying a common spatial pattern (CSP), which is an EEG extraction method, many channels cause an overfit problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a binary particle swarm optimization with channel impact factor in order to select channels close to the most important channels as channel selection method. This paper examines whether or not channel impact factor can improve accuracy by Support Vector Machine(SVM).

• Journal of Families and Better Life
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• v.16 no.4
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• pp.83-83
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• 1998

The purpose of this study is to investigate interpersonal support, tension in lifechanges & satisfaction. The selected sample is composed of 387 housewives in ChongJoo city. SAS pc program was used for the statistical analysis of the data. Data was analyzed by frequency, F-test, percentage, mean, Duncan's Multiple Range Test, Pearson's correlation coefficient, Regression Analysis. Major findings as follows: 1)At wedding & funeral ceremony, kin networks of her parents & parents-in law side were variables to have influence on tension in life changes. And the number of social organization participated were a variable to have influence on the satisfaction. The age of couple, education of couple, duration of marriage, income, family lifecycle, the number of children, pattern of family were variables to influence tension in life changes, but were not variables to influence on the satisfaction. 2) At usual or wedding & funeral ceremony, kin networks of her parents side were variables to influence on instrumental & companionship support. And the number of friends was a variable to influence on companionship & informational support. The number of neighbors was a variable to influence on instrumental, companionship & informational support. The number of social organization participated was a variable to influence on companionship & emotional support. The age of couple, education of couple,income, duration of marriage, family life cycle, number of children, family size, family type were variables to influence on interpersonal support. 3)The relationship between tension and satisfaction in life changes was negative, and between instrumental support and satisfaction was negative also. But between companionship support and satisfaction was positive relationship and between tension of personal &social life and instrumental support was positive relationship. The relationship between tension of marriage life and companionship support was negative and between tension of family life and information support was negative relationships. The received companionship support was lower tension in life changes than not received it. But the received instrumental support was higher tension of personal & social life. The received companionship & informational support was higher satisfaction than not received them. But the received instrumental support was lower satisfaction than not received it. 4) Instrumental & companionship support, at usual kin network of her parents in taw side, at wedding & funeral ceremony kin network of her parents side,were variables to influence on tension in life changes. Instrumental, companionship& informational support, at wedding & funeral ceremony kin network of her parents side, were variables to influence on the satisfaction