• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.122-125
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• 2008

Even though many researchers studied the foot shape and dimension, those applications lacked. The purpose of this study was to develop insole pattern of elderly women according to footprint. Discrepancy in the classification criteria among of foot parameters complicates attempts for elderly women classification of foot sole. To develop a footprint-based classification technique for the classification of foot sole types by allowing simultaneous use of several parameters. Foot sole data from static standing footprints were recorded from 48 elderly women. The factors of footprint shape were determined. Cluster analysis was applied to obtain individual foot sole classifications. The classification model of foot insole is proposed for a classification of footprint in elderly women. An application of ANOVA, Duncan's analysis, frequency analysis, factor analysis, and cluster analysis have been made to footprint data. In order to make clear foot sole characteristics, the factors of footprint shape have been discussed. The results are as follows. The factors of footprint shape have been classified into four types: foot length, sole slope, outside sole slope, and foot width. The types of foot sole shape have been classified into four types: longed, shortened, outside sloped, and toes sloped.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.567-576
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• 2006

In this paper, we applied the multiblock dimension reduction methods to the classification of tumor based on microarray gene expressions data. This procedure involves clustering selected genes, multiblock dimension reduction and classification using linear discrimination analysis and quadratic discrimination analysis.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.184-191
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• 2020

With the emergence of the 4th Industrial Revolution, core technologies that will lead the 4th Industrial Revolution such as AI (artificial intelligence), big data, and Internet of Things (IOT) are also at the center of the topic of the general public. In particular, there is a growing trend of attempts to present future visions by discovering new models by using them for big data analysis based on data collected in a specific field, and inferring and predicting new values with the models. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable, the correlation between the variables, and multicollinearity. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified according to the purpose of analysis. Therefore, in this study, data is classified using a decision tree technique and a random forest technique among classification analysis, which is a machine learning technique that implements AI technology. And by evaluating the degree of classification of the data, we try to find a way to improve the classification and analysis rate of the data.

• ETRI Journal
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• v.43 no.2
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• pp.272-287
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• 2021

This article presents a multi-dimensional spatial pattern analysis of crime events in San Francisco. Our analysis includes the impact of spatial resolution on hotspot identification, temporal effects in crime spatial patterns, and relationships between various crime categories. In this work, crime prediction is viewed as a classification problem. When predictions for a particular category are made, a binary classification-based model is framed, and when all categories are considered for analysis, a multiclass model is formulated. The proposed crime-prediction model (HotBlock) utilizes spatiotemporal analysis for predicting crime in a fixed spatial region over a period of time. It is robust under variation of model parameters. HotBlock's results are compared with baseline real-world crime datasets. It is found that the proposed model outperforms the standard DeepCrime model in most cases.

• Journal of the Korean Geographical Society
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• v.30 no.3
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• pp.296-310
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• 1995

Classification of spatial units into meaningful sets is an important procedure in spatial analysis. It is crucial in characterizing and identifying spatial structures. But traditional classification methods such as cluster analysis require an exact database and impose a clear-cut boundary between classes. Scrutiny of realistic classification problems, however, reveals that available infermation may be vague and that the boundary may be ambiguous. The weakness of conventional methods is that they fail to capture the fuzzy data and the transition between classes. Fuzzy subsets theory is useful for solving these problems. This paper aims to come to the understanding of theoretical foundations of fuzzy spatial analysis, and to find the characteristics of fuzzy classification methods. It attempts to do so through the literature review and the case study of urban classification of the Cities and Eups of Kyung-Nam Province. The main findings are summarized as follows: 1. Following Dubois and Prade, fuzzy information has an imprecise and/or uncertain evaluation. In geography, fuzzy informations about spatial organization, geographical space perception and human behavior are frequent. But the researcher limits his work to numerical data processing and he does not consider spatial fringe. Fuzzy spatial analysis makes it possible to include the interface of groups in classification. 2. Fuzzy numerical taxonomic method is settled by Deloche, Tranquis, Ponsard and Leung. Depending on the data and the method employed, groups derived may be mutually exclusive or they may overlap to a certain degree. Classification pattern can be derived for each degree of similarity/distance $\alpha$. By takina the values of $\alpha$ in ascending or descending order, the hierarchical classification is obtained. 3. Kyung-Nam Cities and Eups were classified by fuzzy discrete classification, fuzzy conjoint classification and cluster analysis according to the ratio of number of persons employed in industries. As a result, they were divided into several groups which had homogeneous characteristies. Fuzzy discrete classification and cluste-analysis give clear-cut boundary, but fuzzy conjoint classification delimit the edges and cores of urban classification. 4. The results of different methods are varied. But each method contributes to the revealing the transparence of spatial structure. Through the result of three kinds of classification, Chung-mu city which has special characteristics and the group of Industrial cities composed by Changwon, Ulsan, Masan, Chinhai, Kimhai, Yangsan, Ungsang, Changsungpo and Shinhyun are evident in common. Even though the appraisal of the fuzzy classification methods, this framework appears to be more realistic and flexible in preserving information pertinent to urban classification.

• Structural Engineering and Mechanics
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• v.75 no.6
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• pp.771-784
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• 2020

Reliability analysis techniques combining with various surrogate models have attracted increasing attention because of their accuracy and great efficiency. However, they primarily focus on the structures with continuous response, while very rare researches on the reliability analysis for structures with discontinuous response are carried out. Furthermore, existing adaptive reliability analysis methods based on importance sampling (IS) still have some intractable defects when dealing with small failure probability, and there is no related research on reliability analysis for structures involving discontinuous response and small failure probability. Therefore, this paper proposes a novel reliability analysis method called AGPC-IS for such structures, which combines adaptive Gaussian process classification (GPC) and adaptive-kernel-density-estimation-based IS. In AGPC-IS, an efficient adaptive strategy for design of experiments (DoE), taking into consideration the classification uncertainty, the sampling uniformity and the regional classification accuracy improvement, is developed with the purpose of improving the accuracy of Gaussian process classifier. The adaptive kernel density estimation is introduced for constructing the quasi-optimal density function of IS. In addition, a novel and more precise stopping criterion is also developed from the perspective of the stability of failure probability estimation. The efficiency, superiority and practicability of AGPC-IS are verified by three examples.

• Journal of the Korean Society of Food Culture
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• v.29 no.2
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• pp.178-186
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• 2014

This study sought to prove the validity of Hansik Yangnyomjang classification Korean culinary education. survey was conducted among Korean Cuisine professionals, culinary instructors, culinary professionals and potential students from various backgrounds. ata were collected by self-administered questionnaires and analyzed by reliability analysis, frequency analysis and t-test. any differences in terms of the validity of Hansik Yangnyomjang classification between groups based on their majors, teaching experiences, and knowledge of sauce classification. First, the result showed that fermented Jang is core element Korean cuisine. Second, Hansik Yangnyomjang classification needs to be organized around Balhyojang. Third, Hansik Yangnyomjang classification for beginners and foreigners who want to learn Korean Cuisine relatively easily. Finally, the term 'sauce' is not suitable for replacing Yangnyomjang.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.566-567
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• 2005

Until now variable pattern classification methods have been introduced. So, variable methods in PD source classification were applied. NN(neural network) the most used scheme as a PD(partial discharge) source classification. But in recent year another method were developed. These methods is present superior to NN in the field of image and signal process function of classification. In this paper, it is show classification result in PD source using three methods; that is, BP(back-propagation), ANFIS(adaptive neuro-fuzzy inference system), PCA-LDA(principle component analysis-linear discriminant analysis).

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.2 s.8
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• pp.181-193
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• 1996

A new technique for land cover classification which applies digital image pre-classified by unsupervised classification technique, clustering, to Canonical Correlation Analysis(CCA) was proposed in this paper. Compared with maximum likelihood classification, the proposed technique had a good flexibility in selecting training areas. This implies that any selected position of training areas has few effects on classification results. Land cover of each cluster designated by CCA after clustering is able to be used as prior information for maximum likelihood classification. In case that the same training areas are used, accuracy of classification using Canonical Correlation Analysis after cluster analysis is better than that of maximum likelihood classification. Therefore, a new technique proposed in this study will be able to be put to practical use. Moreover this will play an important role in the construction of GIS database

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.1
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• pp.25-44
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• 2013

The purpose of this study was to analyze the trends of Classification Studies in Korea from 205 research articles in five scholarly journals in Library and Information Science during the period 1986-2011. Individual article was analyzed in the aspects of specific research topics, research methodologies, data collection & analysis, and the characteristics of classification research was suggested. The amount of research paper has increased during the period and the focus of the study was on practical use and most of the papers were about new classification schedules or modification of current classification systems. Most of the papers were literature research and comparative research on classification systems by professors or graduate students. Top 9 authors in classification research were professors or lecturers and a few authors were computer science major. Therefore, various research topics, research methodologies and collaborations with other disciplines are necessary for future classification research.