• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.595-602
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• 2009

Fuzzy rules, which represent the behavior of their system, are sensitive to fuzzy clustering techniques. If the classification abilities of such clustering techniques are improved, their systems can work for the purpose more accurately because the capabilities of the fuzzy rules and parameters are enhanced by the clustering techniques. Thus, this paper proposes a new hierarchically structured clustering algorithm that can enhance the classification abilities. The proposed clustering technique consists of two clusters based on correlationship and statistical characteristics between data, which can perform classification more accurately. In addition, this paper uses difference data sets to reflect the patterns and regularities of the original data clearly, and constructs multiple fuzzy systems to consider various characteristics of the differences suitably. To verify effectiveness of the proposed techniques, this paper applies the constructed fuzzy systems to the field of time series prediction, and performs prediction for nonlinear time series examples.

• Journal of the Korean Society for information Management
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• v.27 no.2
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• pp.241-252
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• 2010

Titles have been regarded as having effective clustering features, but they sometimes fail to represent the topic of a document and result in poorly generated document clusters. This study aims to improve the performance of document clustering with titles by suggesting titles in the citation bibliography as a clustering feature. Titles of original literature, titles in the citation bibliography, and an aggregation of both titles were adapted to measure the performance of clustering. Each feature was combined with three hierarchical clustering methods, within group average linkage, complete linkage, and Ward's method in the clustering experiment. The best practice case of this experiment was clustering document with features from both titles by within-groups average method.

• Journal of Korea Multimedia Society
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• v.12 no.7
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• pp.997-1005
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• 2009

In this paper, we suggest an energy-efficient clustering scheme based on cross-layer design in wireless sensor networks. The proposed scheme works adequately for the characteristic environment of the networks. In the proposed clustering scheme, we separate clusters composed of sensor nodes in the event area from clusters of the other area when an event occurs by using an application layer information. We can save energy from multiple paths through multiple clusters to deliver the same event. We also suggest TDMA scheduling for non-evented clusters. In the scheduling, we allocate one time slot for each node to save energy. The suggested clustering scheme can increase the lifetime of the entire network. We show that our scheme is energy efficient through simulation in terms of the frequency of event occurrences, the event continual time and the scope.

• Journal of Information Management
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• v.35 no.3
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• pp.125-138
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• 2004

The purpose of this study is to propose a hierarchical user interfaces for browsing the knowledge structure of a single document. To generate the hierarchical knowledge structure, hierarchical term clustering and cluster representative term selection were performed with a single thesis in information science field, and the result was applied to design the interfaces which browse a single document hierarchically using multidimensional scaling. The interfaces can be applied to develop the user-friendly information retrieval system.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10c
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• pp.16-18
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• 2004

무선 센서 네트워크는 센서들을 무선으로 연결하여 데이터 교환을 통해 다양한 환경에서 정보를 수집할 수 있도록 하는 유용한 도구이다. 센서노드 간의 통신은 센서노드의 제한된 에너지를 효율적으로 이용하도록 만들어져야만 한다. 클러스터링에 기반한 계층적 라우팅 기법은 클러스터 내의 노드들은 클러스터헤드와만 통신을 하게하고 클러스터헤드가 한꺼번에 싱크에게 통합된 정보를 보내게 하여 에너지를 절약하는 기법이다. 본 연구에서는 이러한 클러스터링 기반의 라우팅에서 에너지 효율을 높이기 위해 싱크와 클러스터헤드 사이의 거리에 따른 최적 클러스터 크기를 계산하고, 원형의 전체 토폴로지에서 계산된 값과 일치됨을 확인해 보았다.

• Journal of the Korean Society for Library and Information Science
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• v.43 no.3
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• pp.399-416
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• 2009

In this study, clustering methods with related tags were discussed for improving search and exploration in the tag space. The experiments were performed on 10 Delicious tags and the strongly-related tags extracted by each 300 documents, and hierarchical and non-hierarchical clustering methods were carried out based on the tag co-occurrences. To evaluate the experimental results, cluster relevance was measured. Results showed that Ward's method with cosine coefficient, which shows good performance to term clustering, was best performed with consistent clustering tendency. Furthermore, it was analyzed that cluster membership among related tags is based on users' tagging purposes or interest and can disambiguate word sense. Therefore, tag clusters would be helpful for improving search and exploration in the tag space.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.397-401
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• 2008

센서 네트워크는 주변 데이터를 수집하기 위해 널리 퍼뜨려진 작고 저렴한 센서 노드를로 구성된 무선 네트워크 환경이다. 이러한 센서 네트워크는 배치의 편리성과 ad-hoc 적인 연결성, 저렴한 비용의 이 유로 다양한 목적의 응용프로그램에서 사용되고 있다. 그러나 센서 노드의 크기가 제한되기 때문에 센 서 노드의 유용한 전체적인 파워나 전력량의 크기에 제약을 가져온다. 그러므로, 센서 네트워크의 일반 적인 목적은 에너지 효율성을 고려하여 전체 네트워크의 성능을 저하시키지 않고 데이터를 수집하는 것이다. 본 논문에서는 에너지 효율성 증대 및 전체 네트워크의 유지 시간을 증가시키기 위해 각 센서가 가지는 RF 전송 레벨을 이용하여 노드 간의 거리를 통한 계층적 클러스터링 방법을 제안하고 비교 실험을 통해 전체 네트워크에 대한 에너지 효율성을 측정하고자 한다.

• Journal of Korea Multimedia Society
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• v.8 no.7
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• pp.857-868
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• 2005

Schema clustering is important as a prerequisite to the integration of XML schemas. This paper presents an efficient method for clustering XML schemas. The proposed method first computes similarities among schemas. The similarity is defined by the size of the common structure between two schemas under the assumption that the schemas with less cost to be integrated are more similar. Specifically, we extract one-to-one matchings between paths with the largest number of corresponding elements. Finally, a hierarchical clustering method is applied to the value of similarity. Experimental results with many XML schemas show that the method has peformed better compared with previous works, resulting in a Precision of $99\%$ and a rate of clustering of $93\%$ in average.

• Journal of the Korean Society for information Management
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• v.23 no.4 s.62
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• pp.215-231
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• 2006

Recently there are many bibliometric studies attempting to utilize Pathfinder networks(PFNets) for examining and analyzing the intellectual structure of a scholarly field. Pathfinder network scaling has many advantages over traditional multidimensional scaling, including its ability to represent local details as well as global intellectual structure. However there are some limitations in PFNets including very high time complexity. And Pathfinder network scaling cannot be combined with cluster analysis, which has been combined well with traditional multidimensional scaling method. In this paper, a new method named as Parallel Nearest Neighbor Clustering (PNNC) are proposed for complementing those weak points of PFNets. Comparing the clustering performance with traditional hierarchical agglomerative clustering methods shows that PNNC is not only a complement to PFNets but also a fast and powerful clustering method for organizing informations.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10b
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• pp.250-252
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• 1998

내용기반 이미지 검색(Content-based image retrieval)에서 컬러 특징을 표현하기 위해 컬러 히스토그램이 많이 이용되고 있다. 하지만 히스토그램의 고차원적인 성질 때문에 색인구조를 사용한 효율적인 검색이 어렵고, 유사도 계산 단계에서 비용이 많이 든다. 이점을 개선하기 위해서 이미지의 컬러 정보 손실을 최소화하면서 히스토그램의 차원을 낮추는 컬러 클러스터링 방법이 제안되었다. 이 논문은 이미지 검색의 응용 분야에 따른 이미지 데이터의 컬러 분포 특성을 이용한 컬러 클러스터링 방법을 제안한다. 컬러 분포를 가중치로 이용한 계층적 컬러 클러스터링 방법에 대해 알아보고, 두 단계 컬러 히스토그램을 이용한 이미지 검색에 적용하여 컬러 정보 유지 능력을 실험해 본다.