• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.556-578
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• 2020

There are many different researches about how to analyze issues based on real-time news streams. But, there are few researches which analyze issues hierarchically from news articles and even a previous research of hierarchical issue analysis make clustering speed slower as the increment of news articles. In this paper, we propose a hierarchical and incremental clustering for semi real-time issue analysis on news articles. We trained siamese neural network based weighted cosine similarity model, applied this model to k-means algorithm which is used to make word clusters and converted news articles to document vectors by using these word clusters. Finally, we initialized an issue cluster tree from document vectors, updated this tree whenever news articles happen, and analyzed issues in semi real-time. Through the experiment and evaluation, we showed that up to about 0.26 performance has been improved in terms of NMI. Also, in terms of speed of incremental clustering, we also showed about 10 times faster than before.

• Journal of the Korean Society for information Management
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• v.21 no.3
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• pp.251-267
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• 2004

The purpose of this study is to generate the local level knowledge structure of a single document, similar to end-of-the-book indexes and table of contents of printed material through the use of term clustering and cluster representative term selection. Furthermore, it aims to analyze the functionalities of the knowledge structure. and to confirm the applicability of these methods in user-friend1y information services. The results of the term clustering experiment showed that the performance of the Ward's method was superior to that of the fuzzy K -means clustering method. In the cluster representative term selection experiment, using the highest passage frequency term as the representative yielded the best performance. Finally, the result of user task-based functionality tests illustrate that the automatically generated knowledge structure in this study functions similarly to the local level knowledge structure presented In printed material.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.4
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• pp.31-37
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• 2003

This paper is a review of the large-scale information retrieval system, CONDOR. This system was developed by the consortium that consists of Chonbuk National University, Searchline Co. and Carnegie Mellon University. This system is based on the probabilistic model of information retrieval systems. The multi-language query processing, online document summarization based on query and dynamic hierarchy clustering of this system make difference of other systems. We test this system with 30 million web documents successfully.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.106-116
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• 2021

To increase the quality of the system in the subjective-type question grading and document classification, we need the paragraph detection. But it is not easy because it is accompanied by semantic analysis. Many researches on the paragraph detection solve the detection problem using the word based clustering method. However, the word based method can not use the order and dependency relation between words. This paper suggests the paragraph detection system using syntactic-semantic relation between words with the Korean syntactic-semantic analysis. This system is the hybrid system of word based, concept based, and syntactic-semantic tree based detection. The experiment result of the system shows it has the better result than the word based system. This system will be utilized in Korean subjective question grading and document classification.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2108-2115
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• 2000

The conventional Boolean retrieval systems based on vector spae model can provide the results of retrieval fast, they can't reflect exactly user's retrieval purpose including semantic information. Consequently, the results of retrieval process are very different from those users expected. This fact forces users to waste much time for finding expected documents among retrieved documents. In his paper, we designed a bayesian SOM(Self-Organizing feature Maps) in combination with bayesian statistical method and Kohonen network as a kind of unsupervised learning, then perform classifying documents depending on the semantic similarity to user query in real time. If it is difficult to observe statistical characteristics as there are less than 30 documents for clustering, the number of documents must be increased to at least 50. Also, to give high rank to the documents which is most similar to user query semantically among generalized classifications for generalized clusters, we find the similarity by means of Kohonen centroid of each document classification and adjust the secondary rank depending on the similarity.

• 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.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.2
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• pp.26-31
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• 2005

In this paper, we present the document summarizers which are simpler and more condense than the existing ones generally used in the web search engines. This method is a statistic-based summarization method using the concept of the complete graph. We suppose that each sentence as a vertex and the similarity between two sentences as a link of the graph. We compare this summarizer with those of Clustering and MMR techniques which are well-known as the good summarization methods. For the comparison, we use FScore using the summarization results generated by human subjects. Our experimental results verify the accuracy of this method, being about $30\%$ better than the others.

• 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.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.867-874
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• 2004

K-Means algorithm is a non-hierarchical (plat) and reassignment techniques and iterates algorithm steps on the basis of K cluster centroids until the clustering results converge into K clusters. In its nature, K-Means algorithm has characteristics which make different results depending on the initial and new centroids. In this paper, we propose the modified K-Means algorithm which improves the initial and new centroids decision methodologies. By evaluating the performance of two algorithms using the 16 weighting scheme of SMART system, the modified algorithm showed $20{\%}$ better results on recall and F-measure than those of K-Means algorithm, and the document clustering results are quite improved.

• The Journal of Society for e-Business Studies
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• v.19 no.2
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• pp.95-108
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• 2014

Opinion Mining is one of the application domains of text mining which extracting opinions from documents, and much researches are currently underway. Most of related researches focused on the sentiment classification which classifies the documents into positive/negative opinions. However, there is a little interest in extracting the features characterizing the individual product. In this paper, we propose the technique classifying the opinion documents according to the product features, and selecting the those features characterizing each product. In the proposed method, we utilize the document clustering technique and develope a new algorithm for evaluating the similarity between documents. In addition, through experiments, we prove the usefulness of proposed method.