• Journal of the Korean Society for information Management
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• v.18 no.2
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• pp.203-230
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• 2001

The purpose of this study is to develop a document clustering model for automatic classification of knowledge. Two test collections of newspaper article texts and journal article abstracts are built for the clustering experiment. Various feature reduction criteria as well as term weighting methods are applied to the term sets of the test collections, and cosine and Jaccard coefficients are used as similarity measures. The performances of complete linkage and K-means clustering algorithms are compared using different feature selection methods and various term weights. It was found that complete linkage clustering outperforms K-means algorithm and feature reduction up to almost 10% of the total feature sets does not lower the performance of document clustering to any significant extent.

• Journal of the Korean Society for information Management
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• v.29 no.1
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• pp.331-349
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• 2012

Structured abstracts have been regarded as an essential information factor to represent topics of journal articles. This study aims to provide an unconventional view to utilize structured abstracts with the analysis on sub fields of a structured abstract in depth. In this study, a structured abstract was segmented into four fields, namely, purpose, design, findings, and values/implications. Each field was compared in the performance analysis of document clustering. In result, the purpose statement of an abstract affected on the performance of journal article clustering more than any other fields. Furthermore, certain types of keywords were identified to be excluded in the document clustering to improve clustering performance, especially by Within group average clustering method. These keywords had stronger relationship to a specific abstract field such as research design than the topic of an article.

• The KIPS Transactions:PartB
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• v.9B no.1
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• pp.7-16
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• 2002

The Post-clustering algorithms, which cluster the results of Web search engine, have several different requirements from conventional clustering algorithms. In this paper, we propose the new post-clustering algorithm satisfying those requirements as many as possible. The proposed Concept ART is the form of combining the concept vector that have several advantages in document clustering with Fuzzy ART known as real-time clustering algorithms. Moreover we show that it is applicable to general-purpose clustering as well as post-clustering

• Journal of the Korean Society for Library and Information Science
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• v.38 no.4
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• pp.179-192
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• 2004

In this paper, we present that the result of document clustering can change dramatically with respect to the different ways of indexing compound nouns. First of all, the automatic indexing engine specialized for Korean words analysis, which also serves as the backbone engine for automatic document clustering system, is introduced. Then, the details of hierarchical agglomerative clustering(HAC) method, one of the widely used clustering methodologies in these days, was illustrated. As the result of observing the experiments, carried out in the final part of this paper, it comes to the conclusion that the various modes of indexing compound nouns have an effect on the outcome of HAC.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.45-52
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• 2012

This paper proposes a new enhancing document clustering method using the important terms of cluster and the wikipedia. The proposed method can well represent the concept of cluster topics by means of selecting the important terms in cluster by the semantic features of NMF. It can solve the problem of "bags of words" to be not considered the meaningful relationships between documents and clusters, which expands the important terms of cluster by using of the synonyms of wikipedia. Also, it can improve the quality of document clustering which uses the expanded cluster important terms to refine the initial cluster by re-clustering. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• Proceedings of the CALSEC Conference
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• 2005.03a
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• pp.189-194
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• 2005

Many studies have been conducted to classify documents, and to extract useful information from documents. However, most search engines have used a keyword based method. This method does not search and classify documents effectively. This paper identifies structures of XML document based on the fact that the XML document has a structural document using a set theory, which is suggested by Broder, and attempts a test for clustering XML document by applying a k-nearest neighbor algorithm. In addition, this study investigates the effectiveness of the clustering technique for large scaled data, compared to the existing bitmap method, by applying a test, which reveals a difference between the clause based documents instead of using a type of vector, in order to measure the similarity between the existing methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.187-190
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• 2001

Due to the recent proliferation of the internet, it is broadly granted that the necessity of the automatic document categorization has been on the rise. Since it is a heavy time-consuming work and takes too much manpower to process and classify manually, we need a system that categorizes them automatically as their contents. In this paper, we propose the automatic E-mail response system that is based on 2 hierarchical document clustering methods. One is to get the final result from the classifier trained seperatly within each class, after clustering the whole documents into 3 groups so that the first classifier categorize the input documents as the corresponding group. The other method is that the system classifies the most distinct classes first as their similarity, successively. Neural networks have been adopted as classifiers, we have used dendrograms to show the hierarchical aspect of similarities between classes. The comparison among the performances of hierarchical and non-hierarchical classifiers tells us clustering methods have provided the classification efficiency.

• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.109-136
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• 2008

In this survey paper, we discuss biomedical ontologies and major text mining techniques applied to biomedicine and healthcare. Biomedical ontologies such as UMLS are currently being adopted in text mining approaches because they provide domain knowledge for text mining approaches. In addition, biomedical ontologies enable us to resolve many linguistic problems when text mining approaches handle biomedical literature. As the first example of text mining, document clustering is surveyed. Because a document set is normally multiple topic, text mining approaches use document clustering as a preprocessing step to group similar documents. Additionally, document clustering is able to inform the biomedical literature searches required for the practice of evidence-based medicine. We introduce Swanson's UnDiscovered Public Knowledge (UDPK) model to generate biomedical hypotheses from biomedical literature such as MEDLINE by discovering novel connections among logically-related biomedical concepts. Another important area of text mining is document classification. Document classification is a valuable tool for biomedical tasks that involve large amounts of text. We survey well-known classification techniques in biomedicine. As the last example of text mining in biomedicine and healthcare, we survey information extraction. Information extraction is the process of scanning text for information relevant to some interest, including extracting entities, relations, and events. We also address techniques and issues of evaluating text mining applications in biomedicine and healthcare.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.673-676
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• 2004

Fast and high-quality document clustering algorithms play an important role in providing data exploration by organizing large amounts of information into a small number of meaningful clusters. In particular, hierarchical clustering provide a view of the data at different levels, making the large document collections are adapted to people's instinctive and interested requires. Many papers have shown that the hierarchical clustering method takes good-performance, but is limited because of its quadratic time complexity. In contrast, K-means has a time complexity that is linear in the number of documents, but is thought to produce inferior clusters. Think of the factor of simpleness, high-quality and high-efficiency, we combine the two approaches providing a new system named CONDOR system [10] with hierarchical structure based on document clustering using K-means algorithm to "get the best of both worlds". The performance of CONDOR system is compared with the VIVISIMO hierarchical clustering system [9], and performance is analyzed on feature words selection of specific topics and the optimum hierarchy depth.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.625-630
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• 2014

The amount of information is increasing rapidly with the development of the internet and the computer. Since these enormous information is managed by the document forms, it is necessary to search and process them efficiently. The document clustering technique which clusters the related documents through the similarity between the documents help to classify, search, and process the large amount of documents automatically. This paper proposes a method to find the initial seed points through principal component analysis when the documents represented by vectors in the feature vector space are clustered by K-means algorithm in order to increase clustering performance. The experiment shows that our method has a better performance than the traditional K-means algorithm.