• Proceedings of the Korean Society for Information Management Conference
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• 2005.08a
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• pp.65-70
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• 2005

계층적 문서 클러스터링에 있어서 실험집단에 따라 응집식 기법과 분할식 기법의 성능이 다르며, 이를 좌우하는 요소는 분류의 깊이, 즉 분류수준이라고 가정하였다. 조금만 나누면 되는 대분류인 경우는 상대적으로 분할식 기법이 유리하고, 조금만 합치면 되는 소분류인 경우에는 응집식 기법이 유리할 것이라고 판단했기 때문이다. 그에 따라 분할식 클러스터링 기법인 양분(Bisecting) K-means기법과 응집식 기법인 완전연결, 평균연결, WARD기법의 성능을 실험집단이 대분류인 경우와 소분류인 경우의 유사계수를 적용하여 각 기법별 성능을 비교하여 실험집단의 특성에 따른 적합 클러스터링 기법을 찾고자 하였다. 실험결과 응집식 기법과 분할식 기법의 성능 우열에 영향을 미치는 것은 분류수준보다는 변이계수로 측정된 상대적인 군집의 크기 편차인 것으로 나타났다.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.33-40
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• 2003

Data mining plays an important role in a knowledge discovery process and various algorithms of data mining can be selected for the specific purpose. Most of traditional hierachical clustering methode are suitable for processing small data sets, so they difficulties in handling large data sets because of limited resources and insufficient efficiency. In this study we propose a hybrid neural networks clustering technique, called PPC for Pre-Post Clustering that can be applied to large data sets and find unknown patterns. PPC combinds an artificial intelligence method, SOM and a statistical method, hierarchical clustering technique, and clusters data through two processes. In pre-clustering process, PPC digests large data sets using SOM. Then in post-clustering, PPC measures Similarity values according to cohesive distances which show inner features, and adjacent distances which show external distances between clusters. At last PPC clusters large data sets using the simularity values. Experiment with UCI repository data showed that PPC had better cohensive values than the other clustering techniques.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.49-51
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• 2004

최근 GPS시스템, 감시 시스템, 기상 관측 시스템과 같은 다양한 응용 시스템으로부터 수집된 시공간 속성을 가진 데이터를 분석하고자 하는 시공간 데이터 마이닝에 대한 관심이 더욱 높아지고 있다. 기존의 시공간 데이터 마이닝에 대한 연구는 문자.숫자 데이터를 기반의 마이닝 기법을 그대로 적용하고 있기 때문에 데이터의 시공간 속성을 충분히 고려한 분석으로는 한계가 많은 것이 사실이다. 본 논문에서는 패턴 인식과 클러스터링 능력이 뛰어나다고 알려진 SOM을 기반으로 시공간 클러스터링 모듈을 개발하고, 개발된 모듈의 성능과 클러스터링 정확성에 대하여 K-means, 응집 계층 알고리즘(Average Linkage, Ward)과 비교함으로써 시공간 데이터 마이닝을 위한 각 알고리즘들의 성능을 분석하였다 또한 입력 데이터의 특성과 클러스터링 결과를 더욱 정확하게 나타내어 가시적인 분석을 도울 수 있도록 시공간 데이터 클러스터링을 위한 가시화 모듈을 개발하였다.

• The Journal of the Acoustical Society of Korea
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• v.39 no.2
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• pp.120-130
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• 2020

This paper solves the problem in underdetermined convolutive mixture by improving the disadvantages of the multichannel nonnegative matrix factorization technique widely used in blind source separation. In conventional researches based on Spatial Covariance Matrix (SCM), each element composed of values such as power gain of single channel and correlation tends to degrade the quality of the separated sources due to high variance. In this paper, level and frequency normalization is performed to effectively cluster the estimated sources. Therefore, we propose a novel SCM and an effective distance function for cluster pairs. In this paper, the proposed SCM is used for the initialization of the spatial model and used for hierarchical agglomerative clustering in the bottom-up approach. The proposed algorithm was experimented using the 'Signal Separation Evaluation Campaign 2008 development dataset'. As a result, the improvement in most of the performance indicators was confirmed by utilizing the 'Blind Source Separation Eval toolbox', an objective source separation quality verification tool, and especially the performance superiority of the typical SDR of 1 dB to 3.5 dB was verified.

• Korean Journal of Cognitive Science
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• v.22 no.3
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• pp.271-298
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• 2011

The lexical semantic system should be built to grasp lexical semantic information more clearly. In this paper, we studied a semantic clustering of predicates that is one of the steps in building the lexical semantic system. Unlike previous studies that used argument of subcategorization(subject and object), selectional restrictions and interaction information of adverb, we used sense tagged definition in dictionary for the semantic clustering of predicate, and also attempted hierarchical clustering of predicate using the relationship between the generic concept and the specific concept. Most of the predicates in the dictionary were used for clustering. Total of 106,501 predicates(85,754 verbs, 20,747 adjectives) were used for the test. We got results of clustering which is 2,748 clusters of predicate and 130 recursive definition clusters and 261 sub-clusters. The maximum depth of cluster was 16 depth. We compared results of clustering with the Sejong semantic classes for evaluation. The results showed 70.14% of the cohesion.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.1
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• pp.89-104
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• 2021

Based on the TF-IDF weighted value that evaluates the importance of words that play a key role, the semantic network analysis(SNA) was conducted on the self-introduction letter of freshman at Korea National College of Agriculture and Fisheries(KNCAF) in 2020. The top three words calculated by TF-IDF weights were agriculture, mathematics, study (Q. 1), clubs, plants, friends (Q. 2), friends, clubs, opinions, (Q. 3), mushrooms, insects, and fathers (Q. 4). In the relationship between words, the words with high betweenness centrality are reason, high school, attending (Q. 1), garbage, high school, school (Q. 2), importance, misunderstanding, completion (Q.3), processing, feed, and farmhouse (Q. 4). The words with high degree centrality are high school, inquiry, grades (Q. 1), garbage, cleanup, class time (Q. 2), opinion, meetings, volunteer activities (Q.3), processing, space, and practice (Q. 4). The combination of words with high frequency of simultaneous appearances, that is, high correlation, appeared as 'certification - acquisition', 'problem - solution', 'science - life', and 'misunderstanding - concession'. In cluster analysis, the number of clusters obtained by the height of cluster dendrogram was 2(Q.1), 4(Q.2, 4) and 5(Q. 3). At this time, the cohesion in Cluster was high and the heterogeneity between Clusters was clearly shown.