• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.172-174
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• 1999

본 논문에서는 카운셀링을 원하는 사용자가 카운셀러와 전자메일을 통해 상담을 원할 때 사용자의 상담 내용에 근거하여 유사한 사례를 검색해 주는 시스템을 제안한다. 제안방법은 문서의 계층적 클러스터링과 용어 적합성 피드백을 상담 사례 검색 시스템에 적용시켜, 상담사례에 나타나는 단어의 출현 빈도와 유사도를 통해 트리 구조를 형성하고, 이 트리 구조를 통한 하향 탐색을 수행한다. 하향 탐색을 하는 도중 노드의 매칭함수의 값이 서로 유사하여 노드 선택이 어려울 경우, 사용자에게 질의를 통해 용어를 제시하고, 사용자의 피드백을 통해 입력된 사연 내용의 가중치를 개선하여 내용에 가장 부합되는 문서를 탐색한다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.2
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• pp.150-156
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• 2008

Many papers have shown that the hierarchical clustering method takes good-performance, but is limited because of its quadratic time complexity. In contrast, with a large number of variables, K-means reduces a time complexity. Think of the factor of simplify, high-quality and high-efficiency, we combine the two approaches providing a new system named CONDOR system with hierarchical structure based on document clustering using K-means algorithm. Evaluated the performance on different hierarchy depth and initial uncertain centroid number based on variational relative document amount correspond to given queries. Comparing with regular method that the initial centroids have been established in advance, our method performance has been improved a lot.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.06a
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• pp.173-176
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• 2001

본 논문에서는 저차원 제스처 특징 공간에서 연속적인 인간의 제스처 영상을 계층적 클러스터링을 이용하여 인식할 수 있는 방법에 대해 소개한다. 일반적으로 제스처 공간에서 모델 패턴들과 매칭하기 위해서는 모든 모델 영상과 연속적인 입력영상들간의 거리평가로 인식을 수행하게 된다. 여기서 제안한 방법은 모델영상들을 연속성을 가진 클러스터로 분류하여 입력 영상과 계층적으로 비교할 수 있으며 동작에 관한 구체적 정보를 얻을 수 있다. 이 방법은 매칭 속도와 인식률을 개선하고 인식결과를 학습에 이용할 수 있는 장점이 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.355-359
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• 2014

In this paper, we proposed the new fuzzy pattern classifier which combines several fuzzy models with simple consequent parts hierarchically. The basic component of the proposed fuzzy pattern classifier with hierarchical structure is a fuzzy model with simple consequent part so that the complexity of the proposed fuzzy pattern classifier is not high. In order to analyze and divide the input space, we use Fuzzy C-Means clustering algorithm. In addition, we exploit Conditional Fuzzy C-Means clustering algorithm to analyze the sub space which is divided by Fuzzy C-Means clustering algorithm. At each clustered region, we apply a fuzzy model with simple consequent part and build the fuzzy pattern classifier with hierarchical structure. Because of the hierarchical structure of the proposed pattern classifier, the data distribution of the input space can be analyzed in the macroscopic point of view and the microscopic point of view. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.321-327
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• 2006

Recently there has been enormous growth in the amount of commercial and scientific data. Such datasets consist of sequence data that have an inherent sequential nature. However, only a few of the existing clustering algorithms consider sequentiality. This study presents a similarity measure and a method for clustering such sequence datasets. Especially, we present an extended concept of the measure of similarity, which considers various conditions. Using a splice dataset, we show that the quality of clusters generated by our proposed clustering algorithm is better than that of clusters produced by traditional clustering algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.217-222
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• 2015

Clustering is one of important data mining techniques known as exploratory data analysis and is being applied in various engineering and scientific fields such as pattern recognition, remote sensing, and so on. The method organizes data by abstracting underlying structure either as a grouping of individuals or as a hierarchy of groups. Weather radar observes atmospheric objects by utilizing reflected signals and stores observed data in corresponding coordinate. To analyze the radar data, it is needed to be separately organized precipitation and non-precipitation echo based on similarities. Thus, this paper studies to apply clustering method to radar data. In addition, in order to solve the problem when precipitation echo locates close to non-precipitation echo, fuzzy logic based clustering method which can consider both distance and other properties such as reflectivity and Doppler velocity is suggested in this paper. By using actual cases, the suggested clustering method derives better results than previous method in near-located precipitation and non-precipitation echo case.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.103-105
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• 2004

계층적 개념 트리는 개념의 전체적인 구조를 제공하여 사용자의 이해를 돕는다. 이러한 계층적 개념 트리는 특정 분야의 전문가나 지식 공학자, 혹은 개인에 의해서 제공되어 왔다. 하지만 계층적 개념 트리를 구축하기 위해서는 않은 시간과 노력이 요구된다. 따라서 계층적 개념 트리를 자동으로 구축하기 위한 시스템 필요하게 되었다. 이 논문에서는 정보 검색 틴 온톨로지 연구에 있어서 계층적 개념 트리를 자동으로 구축하기 위한 기존 연구에 대해서 알아보고, 개념적 클러스터링 방법인 FCA(Formal Concept Analysis)를 사용하여 문서로부터 계층적 개념 트리를 구축하는 방법을 제안하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.345-348
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• 2003

본 논문에서는 이 문서 클러스터링 방법 중 계층적 방법인 Kmeans 클러스터링 알고리즘을 이용하여 문서를 클러스터링 하고자 한다 기존의 Kmeans 클러스터링 알고리즘은 문서의 수가 많을 경우 하나의 클러스터링에 너무 많은 문서들이 할당되는 문제점이 있다. 이 치우침을 완화하고자 각 클러스터링에 할당된 문서 수에 따라서 문서에 가중치를 부여한 후 다시 클러스터링을 하는 방법을 제안하였다. 실험 결과는 정확률, 재현율을 결합한 조화 평균(F-measure)를 사용하여 평가하였으며 기존 알고리즘보다 9%이상의 성능 향상을 나타냈다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1222-1227
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• 2009

In this paper, we proposed a new method, the CACH(Context-aware Clustering Hierarchy) algorithm in Mobile Ad-hoc Network(MANET) systems. The proposed CACH algorithm based on hybrid and clustering protocol that provide the reliable monitoring and control of a variety of environments for remote place. To improve the routing protocol in MANET, energy efficient routing protocol would be required as well as considering the mobility would be needed. The proposed analysis could help in defining the optimum depth of hierarchy architecture CACH utilize. Also, the proposed CACH could be used localized condition to enable adaptation and robustness for dynamic network topology protocol and this provide that our hierarchy to be resilient. As a result, our simulation results would show that a new method for CACH could find energy efficient depth of hierarchy of a cluster.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.744-748
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• 2008

In this paper, we propose a novel technique that generate a new song with given example songs. Our system use k-th order Markov chain of which each state represents notes in a measure. Because we have to consider very high-dimensional space if we use notes in a measure as a state of Markov chain directly, we exploit a hierarchical clustering technique for given example songs to use each cluster as a state. Each given examples can be represented as sequences of cluster ID, and we use them for training data of the Markov chain. The resulting Markov chain effectively gives new song similar to given examples.