• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.73-80
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• 2008

In this paper, an advertisement-keyword finding method using document clustering is proposed to solve problems by ambiguous words and incorrect identification of main keywords. News articles that have similar contents and the same advertisement-keywords are clustered to construct the contextual information of advertisement-keywords. In addition to news articles, the web page and summary of a product are also used to construct the contextual information. The given document is classified as one of the news article clusters, and then cluster-relevant advertisement-keywords are used to identify keywords in the document. We could achieve 21% precision improvement by our proposed method.

• The Journal of Korean Association of Computer Education
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• v.11 no.3
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• pp.67-80
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• 2008

Various document categorization methods have been studied to provide a user with an effective way of browsing a large scale of documents. They do compares set of documents into groups of semantically similar documents automatically. However, the automatic categorization method suffers from low accuracy. This thesis proposes a semi-automatic document categorization method based on the domains of documents. Each documents is belongs to its initial domain. All the documents in each domain are recursively clustered in a level-wise manner, so that the category tree of the documents can be founded. To find the clusters of documents, the stop-word of each document is removed on the document frequency of a word in the domain. For each cluster, its cluster keywords are extracted based on the common keywords among the documents, and are used as the category of the domain. Recursively, each cluster is regarded as a specified domain and the same procedure is repeated until it is terminated by a user. In each level of clustering, a user can adjust any incorrectly clustered documents to improve the accuracy of the document categorization.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.1-8
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• 2007

As the web document of XML, an exchange language of data in the advanced Internet, is increasing, a target of information retrieval becomes the web documents. Therefore, there we researches on structure, integration and retrieval of XML documents. This paper proposes a clustering method of XML documents based on frequent structures, as a basic research to efficiently process query and retrieval. To do so, first, trees representing XML documents are decomposed and we extract frequent structures from them. Second, we perform clustering considering the weight of large items to adjust cluster creation and cluster cohesion, considering frequent structures as items of transactions. Third, we show the excellence of our method through some experiments which compare which the previous methods.

• Journal of the Korean Society for Library and Information Science
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• v.39 no.4
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• pp.105-118
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• 2005

As the number of users who ask and give answers in the query-answer documents retrieval system is growing exponentially, the query-answer document become a crucial information resource, as a new type of information retrieval service. A query-answer document Consists of three structural parts : a query, explanation on query, and answers Chosen by users who asked the query. To identify the role of each structural part in representing the topics of documents, the three structural parts were clustered automatically and the results of several clustering tests were compared in this study.

• The Journal of the Korea Contents Association
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• v.9 no.3
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• pp.1-8
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• 2009

This paper proposes a new document clustering system using fuzzy logic-based genetic algorithm (GA) and semantic vector expansion technology. It has been known in many GA papers that the success depends on two factors, the diversity of the population and the capability to convergence. We use the fuzzy logic-based operators to adaptively adjust the influence between these two factors. In traditional document clustering, the most popular and straightforward approach to represent the document is vector space model (VSM). However, this approach not only leads to a high dimensional feature space, but also ignores the semantic relationships between some important words, which would affect the accuracy of clustering. In this paper we use latent semantic analysis (LSA)to expand the documents to corresponding semantic vectors conceptually, rather than the individual terms. Meanwhile, the sizes of the vectors can be reduced drastically. We test our clustering algorithm on 20 news groups and Reuter collection data sets. The results show that our method outperforms the conventional GA in various document representation environments.

• Journal of information and communication convergence engineering
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• v.4 no.4
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• pp.174-179
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• 2006

In this paper we present a text detection method inspired by wavelet packet analysis and improved fuzzy clustering algorithm(IAFC).This approach assumes that the text and non-text regions are considered as two different texture regions. The text detection is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multi scale features, we adapt the improved fuzzy clustering algorithm based on the unsupervised learning rule. The results show that our text detection method is effective for document images scanned from newspapers and journals.

• Journal of Korean Library and Information Science Society
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• v.34 no.3
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• pp.211-229
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• 2003

In this paper, we have focused that the number of word in the web document affects definite clustering performance. Our experimental results have clearly shown the relationship between the amounts of word and its impact on clustering performance. We also have presented an algorithm that can be supplemented of the contrast portion through co-links frequency of web documents. Testing bench of this research is 1,449 web documents included on 'Natural science' category among the Naver Directory. We have clustered these objects by term-based clustering, link-based clustering, and hybrid clustering method, and compared the output results with originally allocated category of Naver directory.

• Journal of KIISE:Software and Applications
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• v.36 no.5
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• pp.347-352
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• 2009

Nonnegative matrix factorization (NMF) is a popular method for multivariate analysis of nonnegative data, the goal of which is decompose a data matrix into a product of two factor matrices with all entries in factor matrices restricted to be nonnegative. NMF was shown to be useful in a task of clustering (especially document clustering). In this paper we present an algorithm for orthogonal nonnegative matrix factorization, where an orthogonality constraint is imposed on the nonnegative decomposition of a term-document matrix. We develop multiplicative updates directly from true gradient on Stiefel manifold, whereas existing algorithms consider additive orthogonality constraints. Experiments on several different document data sets show our orthogonal NMF algorithms perform better in a task of clustering, compared to the standard NMF and an existing orthogonal NMF.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.12
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• pp.2335-2342
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• 2006

The current web is accumulating abundant information. In particular, text based documents are a type used very easily and frequently by human. So, numerous researches are progressed to retrieve the text documents using many methods, such as probability, statistics, vector similarity, Bayesian, and so on. These researches however, could not consider both subject and semantic of documents. So, to overcome the previous problems, we propose the document similarity method for semantic retrieval of document users want. This is the core method of document clustering. This method firstly, expresses a hierarchy semantically of document content ut gives the important hierarchy domain of document to weight. With this, we could measure the similarity between documents using both the domain weight and concepts coincidence in the domain hierarchies.

• Journal of the Korean Society for information Management
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• v.21 no.3
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• pp.289-303
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• 2004

This experimental study proposes a multi-document summarization method that produces optimal summaries in which users can find answers to their queries. In order to identify the most effective method for this purpose, the performance of the three summarization methods were compared. The investigated methods are sentence clustering, passage extraction through spreading activation, and clustering-passage extraction hybrid methods. The effectiveness of each summarizing method was evaluated by two criteria used to measure the accuracy and the redundancy of a summary. The passage extraction method using the sequential bnb search algorithm proved to be most effective in summarizing multiple documents with regard to summarization precision. This study proposes the passage extraction method as the optimal multi-document summarization method.