• Journal of Sasang Constitutional Medicine
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• v.7 no.2
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• pp.89-100
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• 1995

This study was conducted for the purpose of finding out an objective classification method for Sasang Constitutional medicine, which divides people into 4 groups of constitution and presents comprehensively physiology, pathology, diagnosis, therapy and recuperation regarding each constitution. Questionnaire of Sasang Constitution Classification(I) was administered to 328 inpatients at Kyung Hee Oriental Medicine Hospital. Data was collected during 10 months from June 1994 to Mar. 11, 1995. For the purposes of this study, the collected data was analyzed by crosstabs, variation analysis, and discrimination analysis. The analyzing program was SPSS PC+V4.0. For the purposes of this study, the collected data was analyzed by crosstabs, cariation analysis, and discrimination analysis. The analyzing program was SPSS PC+V4.0. The results were as follows : 1. There was significant differences of each group scales through variation analysis. The questions of each group had Sasang constitutional diagnostic discrimination abilities 2. The diagnostic discrimination abilities(Hit-ratio=56.10%) of the Questionnaire for Sasang Constitution Classification(I) were found to have over 20% improvement than the propotional chance criteria(33.3%), Especially, Hit-ratio for $So{\breve{u}}m$-In(63%) was higher than that of SoYang-In(55%) and $Ta{\breve{e}}um$-In(56.3%). 3. Through discrimination analysis on good questions of each group, the diagnostic discrimination abilities of the Questionnaire for Sasang Constitution Classification(I) was 57.93%. 4. This would be on the ground that the Questionnaire for Sasang Constitution Classification(I) could be used as a tool for Sasang constitution classification.

• Journal of Environmental Science International
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• v.12 no.12
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• pp.1215-1225
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• 2003

The purpose of this research was to investigate the characteristics of urban ecotopes and to classify ecotopes systematically from them. Total of 15 characteristics for classification of ecotopes were selected, and there were categorized 3 factors, that is abiotic, biotic and anthropological factors. The ecotope types in the study area were classified into 67. The classification of ecotope was made with SPSS for Windows Version 10.0 on the basis of the 15 characteristics. As the results of cluster analysis using the average linkage method between groups, groups of ecotope type were divided into 15 clusters. It was known that there was not a great difference in an affinity as the result of overlapping the maps of ecotope type and land use type. This research suggested characteristics for classification of ecotopes, but there was a limit to Set the objective method for grade classification because of lacking in the basic data, the research of characteristics will be accomplished continuously.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.5
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• pp.463-470
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• 2015

Solar cell is typical representative of renewable green energy. Silicon wafer contributes about 66 percent to its cost structure. In its manufacturing, micro-cracks are often occurred due to manufacturing process such as wire sawing, grinding and cleaning. Their detection and classification are important to process feedback information. In this paper, a classification method of micro-cracks is proposed, based on the fusion of principal component analysis(PCA) and neural network. The proposed method shows that it gives higher results than single application of two methods, in terms of shape and size classification of micro-cracks.

• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.245-248
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• 2007

In this paper, we research the content-based analysis and classification according to the composition of the feature parameter data base for the audio data to implement the audio data index and searching system. Audio data is classified to the primitive various auditory types. We described the analysis and feature extraction method for the feature parameters available to the audio data classification. And we compose the feature parameters data base in the index group unit, then compare and analyze the audio data centering the including level around and index criterion into the audio categories. Based on this result, we compose feature vectors of audio data according to the classification categories, and simulate to classify using discrimination function.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.102-108
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• 2005

This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to heart disease medical data mining. The data we study is collected from patients with coronary heart disease. It has 1,723 records of 71 attributes each. We use the system-reconstruction method to weight it. We use decision tree algorithms, such as induction of decision trees (ID3), classification and regression tree (C4.5), classification and regression tree (CART), Chi-square automatic interaction detector (CHAID), and exhausted CHAID. We use the results to compare the correction rate, leaf number, and tree depth of different decision-tree algorithms. According to the experiments, we know that weighted data can improve the correction rate of coronary heart disease data but has little effect on the tree depth and leaf number.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.53-66
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• 2008

This research is to the establishment of a conceptual framework that supports road characteristic classification from a new point of view in order to complement of the existing road functional classification and examine of traffic pattern. The road characteristic classification(RCC) is expected to use important performance criteria that produced a policy guidelines for transportation planning and operational management. For this study, the traffic data used the permanent traffic counters(PTCs) located within the national highway between 2002 and 2006. The research has described for a systematic review and assessment of how exploratory factor analysis should be applied from 12 explanatory variables. The optimal number of components and clusters are determined by interpretation of the factor analysis results. As a result, the scenario including all 12 explanatory variables is better than other scenarios. The four components is produced the optimal number of factors. This research made contributions to the understanding of the exploratory factor analysis for the road characteristic classification, further applying the objective input data for various analysis method, such as cluster analysis, regression analysis and discriminant analysis.

• Fashion & Textile Research Journal
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• v.6 no.2
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• pp.221-228
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• 2004

The purpose of this study was to compare somatotypes from various classification methods, to analyze the interrelation among each somatotype or each high frequency type, and to suggest the basis to interpret body size and shape more accurately. As a sample, the subjects were 97 Korean females between 18 and 24 years old. They were measured both anthropometric and photographic measuring in November, 1999. Their somatotypes were classified by three kinds of classification methods. The first method was based on the lateral view of body, the second involved Factor and Cluster analysis with the photographic measurements of anterior and lateral body, and the third involved Factor and Cluster analysis with the anthropometric measurements of whole body. The upper body was classified into three types, and the lower body was classified into 6 types from the lateral view of body. The bend-forward/q-2 was found to be the 'High-frequency type from the lateral view of body', and the Straight/n-1 was found to be the 'Straight type from the lateral view of body'. From the classification by the analysis of photographic measurements, the anterior body was classified into three types, the lateral was classified into 4 types. The X/${\varepsilon}$ type was found to be the 'High-frequency type from the analysis of photographic measurements of anterior and lateral body'. From the classification by the analysis of anthropometric measurements, the whole body was classified into three types. The i type was found to be the 'High-frequency type from the analysis of anthropometric measurements of whole body'. The significant interrelation was certified among some somatotypes or some High-frequency types. We found that both the view of body and the statistical analysis would make the clear definition of each somatotype possible. In order to certify the representativeness of High-frequency type, further analysis would be required of subjects who were in the High-frequency type and their body parts were in the High-frequency range.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.31-34
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• 1996

The extensive researches have compared the performance of neural networks(NN) with those of various statistical techniques for the classification problem. The empirical results of these comparative studies have indicated that the neural networks often outperform the traditional statistical techniques. Moreover, there are some efforts that try to combine various classification methods, especially multivariate discriminant analysis with neural networks. While these efforts improve the performance, there exists a problem violating robust assumptions of multivariate discriminant analysis that are multivariate normality of the independent variables and equality of variance-covariance matrices in each of the groups. On the contrary, cluster analysis alleviates this assumption like neural networks. We propose a new approach to classification problems by combining the cluster analysis with neural networks. The resulting predictions of the composite model are more accurate than each individual technique.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.1-5
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• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• Journal of Korean Library and Information Science Society
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• v.48 no.2
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• pp.373-403
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• 2017

The purpose of this study is to establish the classification principle of archives for the May 18th democratic uprising in terms of preservation and utilization of it and to develop an integrated classification system for it. For this purpose, it was carried out by the previous research on the classification of records and institutional case analysis. Also, we developed an integrated provenance-based classification system based on the practical analysis on the data held in 3 representative institutions in Gwangju. This classification system was proposed by facets of 'provenance-material-period-media-subject' type. We also proposed the collection-based integrated classification system that reflects on the expansion of archivists' role and the trend of times.