• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.103-109
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• 2018

In this paper, we propose a dynamic document classification method which breaks away from existing document classification method with artificial categorization rules focusing on suppliers and has changing categorization rules according to users' needs or social trends. The core of this dynamic document classification method lies in the fact that it creates classification criteria real-time by using topic modeling techniques without standardized category rules, which does not force users to use unnecessary frames. In addition, it can also search the details through the relevance analysis by calculating the relationship between the words that is difficult to grasp by word frequency alone. Rather than for logical and systematic documents, this method proposed can be used more effectively for situation analysis and retrieving information of unstructured data which do not fit the category of existing classification such as VOC (Voice Of Customer), SNS and customer reviews of Internet shopping malls and it can react to users' needs flexibly. In addition, it has no process of selecting the classification rules by the suppliers and in case there is a misclassification, it requires no manual work, which reduces unnecessary workload.

• Journal of Korea Multimedia Society
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• v.4 no.2
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• pp.171-181
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• 2001

The previous Bayesian document categorization method has problems that it requires a lot of time and effort in word clustering and it hardly reflects the semantic information between words. In this paper, we propose a weighted Bayesian document categorizing method based on association word knowledge base acquired by mining technique. The proposed method constructs weighted association word knowledge base using documents in training set. Then, classifier using Bayesian probability categorizes documents based on the constructed association word knowledge base. In order to evaluate performance of the proposed method, we compare our experimental results with those of weighted Bayesian document categorizing method using vocabulary dictionary by mutual information, weighted Bayesian document categorizing method, and simple Bayesian document categorizing method. The experimental result shows that weighted Bayesian categorizing method using association word knowledge base has improved performance 0.87% and 2.77% and 5.09% over weighted Bayesian categorizing method using vocabulary dictionary by mutual information and weighted Bayesian method and simple Bayesian method, respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.10-16
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• 2010

XML is increasingly important in data exchange and information management. A large amount of efforts have been spent in developing efficient techniques for accessing, querying, and storing XML documents. In this paper, we propose a new method to cluster XML documents efficiently. A bit vector which represents a XML document is proposed to cluster the XML documents. The similarity between two XML documents is measured by a bit-wise AND operation between two corresponding bit vectors. The experiment shows that the clusters are formed well and efficiently when a bit vector is used for the feature of a XML document.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.393-398
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• 2001

특정 영역에 대해 사용자에게 관련 정보를 제공해 주는 서비스를 하는 정보 에이전트를 개발 중이다. 정보 에이전트는 사용자 질의 처리를 달은 Agent Manager와 지식베이스를 관리하는 KB Manager, 그리고 Web으로부터 해당 영역의 관련 문서를 끌어오는 Web Manager로 구성되어 있다. Web Manager는 방문할 URL을 수집하고, 이들 문서에 대한 관련 평가와 Indexing을 수행한다. Web Manager는 검색 엔진을 이용하거나, 방문한 문서의 link를 이용하여 URL을 수집하는데 이러한 URL수집기법은 많은 관련 문서를 놓치는 문제점이 있다. 이 문제점을 해결하기 위해서 해당 영역과 관련된 Site들을 대상으로 Link를 이용해 문서들을 모아와, 문서들을 TAG들의 패턴으로 얻어낸 문서 형식을 이용해 Clustering하며 관련 문서들의 Group을 찾아내는 적극적 문서 수집 기법을 제안한다. 실험 결과, Link와 Clustering을 이용할 경우 기존보다 효과적으로 관련 문서를 많이 수집할 수 있음을 알 수 있다.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.19-30
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• 2009

The service that has recently come into the spotlight utilizes the map to first approach the map and then provide various mash-up formed results through the interface. This service can provide precise information to the users but the map is barely reusable. The sketch-map system of this paper, unlike the existing large map system, uses the method of presenting the specific spot and route in XML document and then clustering among sketch-maps. The map service system is designed to show the optimum route to the destination in a simple outline map. It is done by renovating the spot presented by the map into optimum contents. This service system, through the process of analyzing, splitting and clustering of the sketch-map's XML document input, creates a valid form of a sketch-map. It uses the LCS(Longest Common Subsequence) algorithm for splitting and merging sketch-map in the process of query. In addition, the simulation of this system's expected effects is provided. It shows how the maps that share information and knowledge assemble to form a large map and thus presents the system's ability and role as a new research portal.

• The Journal of the Korea Contents Association
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• v.7 no.5
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• pp.16-26
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• 2007

Because it is difficult on existing document retrieval systems to visualize the search result, search results show document titles and short summaries of the parts that include the search keywords. If the result list is long, it is difficult to examine all the documents at once and to find a relation among them. This study uses clustering to classify similar documents into groups to make it easy to grasp the relations among the searched documents. Also, this study proposes a two-level visualization algorithm such that, first, the center of clusters is projected to low-dimensional space by using multi-dimensional scaling to help searchers grasp the relation among clusters at a glance, and second, individual documents are drawn in low-dimensional space based on the center of clusters using the orbital model as a basis to easily confirm similarities among individual documents. This study is tested on the benchmark data and the real data, and it shows that it is possible to visualize search results in real time.

• Journal of the Korea Society of Computer and Information
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• v.7 no.4
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• pp.94-101
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• 2002

It expected a lot of changes in mass media and documentation expression as documents on web are getting diverse, complex and massive. An Annals of The Chosun Dynasty is a very important document used for researching historical facts and is published as CD-Rom. However. The CD-Rom was composed as content-based and using simple search method, therefore it's very difficult to make determine event-relationship between documents factors. Because of that, we studied to discover event-relationship between documents through clustering and efficient similarity method among Annals of The Chosun Dynasty. For the research method, we discovered the best similarity method for historical written documents through simulation similarity measures of Annals of The Chosun Dynasty documents. Then we did simulation-clustering documents based on similarity probability. In evaluation of the clustered documents , the results were the same as when manually figured.

• Journal of Korea Multimedia Society
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• v.14 no.6
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• pp.730-741
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• 2011

With the development of the internet and computer technology, the amount of information through the internet is increasing rapidly and it is managed in document form. For this reason, the research into the method to manage for a large amount of document in an effective way is necessary. The conventional document categorization method used only the keywords of related documents for document classification. However, this paper proposed keyword extraction method of based on association rule. This method extracts a set of related keywords which are involved in document's category and classifies representative keyword by using the classification rule proposed in this paper. In addition, this paper proposed the preprocessing method for efficient keywords creation and predicted the new document's category. We can design the classifier and measure the performance throughout the experiment to increase the profile's classification performance. When predicting the category, substituting all the classification rules one by one is the major reason to decrease the process performance in a profile. Finally, this paper suggested automatically categorizing plan which can be applied to hierarchical category architecture, extended from simple category architecture.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1056-1059
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• 2003

Self-Oranizing Map(SOM) is an unsupervised neural network providing cluster analysis of high dimensional input data. The output from the SOM is represented in map that help us to explore data. The weak point of conventional SOM is when the map is large, it take a long time to train the data. The computing time is known to be O(MN) for trainning to find the winning node (M,N are the number of nodes in width and height of the map). This paper presents a new method to reduce the computing time by creating new map. Each node in a new map is the centroid of nodes' group that are in the original map. After create a new map, we find the winning node of this map, then find the winning node in original map only in nodes that are represented by the winning node from the new map. This new method is called "High Speed Self-Oranizing Map"(HS-SOM). Our experiment use HS-SOM to cluster documents and compare with SOM. The results from the experiment shows that HS-SOM can reduce computing time by 30%-50% over conventional SOM.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.3
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• pp.1-7
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• 2012

Recently, researches are studied in developing efficient techniques for accessing, querying, and managing XML documents which are frequently used in the Internet. In this paper, we propose a new method to cluster XML documents efficiently. An element of a XML document corresponds to a node of the corresponding tree and an inclusion relationship of the document corresponds to a relationship between parent and child node of the tree. Therefore, similar XML documents are similar to the node's name and level of the corresponding trees. We make evaluation function with this characteristic to cluster XML documents by genetic algorithm. The experiment shows that our proposed method has better performance than other existing methods.