• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.681-691
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• 2006

Clustering is one of widely used methods for grouping thousands of genes by their similarities of expression levels, so that it helps to analyze gene expression profiles. This method has been used for identifying the functions of genes. Fuzzy clustering method, which is one category of clustering, assigns one sample to multiple groups according to their degrees of membership. This method is more appropriate for analyzing gene expression profiles because single gene might involve multiple genetic functions. Clustering methods, however, have the problems that they are sensitive to initialization and can be trapped into local optima. To solve these problems, this paper proposes an evolutionary fuzzy clustering method, where adaptive a-cut based evaluation is used for the fitness evaluation to apply different criteria considering the characteristics of datasets to overcome the limitation of Bayesian validation method that applies the same criterion to all datasets. We have conducted experiments with SRBCT and yeast cell-cycle datasets and analyzed the results to confirm the usefulness of the proposed method.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.633-650
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• 2013

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing values with cluster centers, as part of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques employ K-Means or K-Medoids and Multi Layer Perceptron (MLP)or GRNN in Stage-1and Stage-2respectively. Several experiments were conducted on 8benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.159-168
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• 2004

This paper presents hybrid classification method to improve the performance of satellite images classification by combining Bayesian maximum likelihood classifier, ISODATA clustering and fuzzy C-Means algorithm. In this paper, the training data of each class were generated by separating the spectral signature using ISODATA clustering. We can classify according to pixel's membership grade followed by cluster center of fuzzy C-Means algorithm as the mean value of training data for each class. Bayesian maximum likelihood classifier is performed with prior probability by result of fuzzy C-Means classification. The results shows that proposed method could improve performance of classification method and also perform classification with no concern about spectral signature of the training data. The proposed method Is applied to a Landsat TM satellite image for the verifying test.

• Journal of the Korean Society of Clothing and Textiles
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• v.23 no.4
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• pp.563-574
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• 1999

The purpose of this study was to provide basic information for obese boy's clothing construction that can reflect the characteristics of their bodies. The subjects for anthropometric measurements which were performed directly were obese boys of 9 to 11 year-old. To classify the somatotype and to analyze the characteristics of each somatotype 310 obese boys were examined. Data were analyzed by using multivariate method, By means of Ward the subjects were classified into 4 clusters according to the factor scores which were obtained from 6 factors providing the information of 54 items. 4 clusters were identified. 1) Type I was characterized by tall and obese type 2) Type II was characterized by short and small type 3) Type III was characterized by long and obese type of lower body. 4) Type IV was characterized by short and obese type.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.303-311
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• 1999

The amount of financial information in today's sophisticated large data bases is huge and makes comparisons between company performance difficult or at least very time consuming. The purpose of this paper is to investigate whether neural networks in the form of self-organizing maps can be used to manage the complexity in large data bases. This paper structures and analyzes accounting numbers in a large data base over several time periods. By using self-organizing maps, we overcome the problems associated with finding the appropriate underlying distribution and the functional form of the underlying data in the structuring task that is often encountered, for example, when using cluster analysis. The method chosen also offers a way of visualizing the results. The data base in this study consists of annual reports of more than 80 Korean companies with data from the year 1998.

• Journal of Haehwa Medicine
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• v.23 no.1
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• pp.115-124
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• 2014

Objectives : There are plenty of medical record of acupuncture & moxibustion in Traditional East Asian medicine(TEAM). We performed this study to find out the hidden criteria lies on this record to choose proper acupoints. Methods : "Zhenjiuzishengjing", ancient TEAM book was analysed using document clustering techniques. Corpus was made from this book. It contained 196 texts driven from each symptoms. Each texts converted to vector representing frequency of 349 acupoints. Distance of vectors calculated by weighted Euclidean distance method. According to this distances, hierarchical clustering of symptoms was builded. Results : The cluster consisted of five large groups. they had high corelation with body part; head and face, chest, abdomen, upper extremity, lower extremity, back. Conclusions : It assumes that body part of symptom is the most importance criteria of acupoints selecting. some high similar symptom vectors consolidated this result. the other criteria is cause and pathway of illness. some symptoms bound together which had common cause and pathway.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.167-175
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• 2016

The volatile component analysis of 82 commercialized liquors(44 samples of single malt whisky, 20 samples of blended whisky and 18 samples of brandy) was carried out by gas chromatography after liquid-liquid extraction with dichloromethane. Pattern recognition techniques such as principle component analysis(PCA), cluster analysis(CA), linear discriminant analysis(LDA) and partial least square discriminant analysis(PLSDA) were applied for the discrimination of different liquor categories. Classification rules were validated by considering sensitivity and specificity of each class. Both techniques, LDA and PLSDA, gave 100% sensitivity and specificity for all of the categories. These results suggested that the common characteristics and identities as typification of whiskies and brandys was founded by using multivariate data analysis method.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.821-834
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• 2013

Law et al. (2004) proposed a normal distribution based salient mixture model for variable selection in clustering. However, this model has substantial problems such as the unidentifiability of components an the inaccurate selection of informative variables in the case of a small cluster size. We propose an alternative method to overcome problems and demonstrate a good performance through experiments on simulated data and real data.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.449-454
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• 2006

최근 PC 클러스터를 이용한 초고해상도 영상표현 시스템(예: PowerWall$^{TM}$) 혹은 몰입형 가상환경 표현시스템 (예: CAVE$^{TM}$, RealityCenters$^{TM}$) 등과 같은 멀티프로젝션 디스플레이 시스템은 산업, 군사, 과학, 의학 등에 널리 활용되고 있다. 하지만, 이와 같은 멀티프로젝션 디스플레이 시스템은 다수의 PC 클러스터와 프로젝터의 연결에 의해 구성이 되기 때문에 그 제어 방법은 각각의 PC 및 프로젝터의 프로그램과 동작을 반복적으로 실행하여야 한다. 이에 PC 클러스터 및 프로젝터의 직관적인 제어가 가능하고 일괄적으로 운용할 수 있는 환경이 필요하다. 본 연구에서는 멀티프로젝션 디스플레이 시스템의 PC 클러스터 및 프로젝터의 제어와 운용에 관한 것으로, PC 클러스터에 필요한 응용프로그램 일괄실행, 일괄 power 처리와 프로젝터에 필요한 일시중지(Mute), 입력소스선택, 일괄 power on/off 수행 등에 관한 효과적인 인터페이스의 구현 및 제어 방법을 제시하고자 한다.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.4
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• pp.400-404
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• 2006

We suggest a method to detect rear car license plate of one more cars by using blobs. First, we try to search all of the blobs from an input image based on the difference between objects and background. Second, we obtain rectangles enclosed the blobs, and rectangle clusters by considering the properties, for example, the number, size, distance, position. Third, the cluster is verified by the Support Vector Machine. Even if we only use the adaptive binarization as the preprocessing, the detection ratio is very high.