• Proceedings of the IEEK Conference
• /
• 2004.08c
• /
• pp.885-889
• /
• 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. Hierarchical clustering improves the performance of retrieval and makes that users can understand easily. For outperforming of clustering, we implemented hierarchical structure with variety and readability, by careful selection of cluster topic words and deciding the number of clusters dynamically. It is important to select topic words because hierarchical clustering structure is summarizes result of searching. We made choice of noun word as a cluster topic word. The quality of topic words is increased $33\%$ as follows. As the topic word of each cluster, the only noun word is extracted for the top-level cluster and the used topic words for the children clusters were not reused.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.5
• /
• pp.30-38
• /
• 2011

This paper proposes a new enhancing document clustering method using a synonym of cluster topic and the similarity. The proposed method can well represent the inherent structure of document cluster set by means of selecting terms of cluster topic based on the semantic features by NMF. It can solve the problem of "bags of words" by using of expanding the terms of cluster topics which uses the synonyms of WordNet. Also, it can improve the quality of document clustering which uses the cosine similarity between the expanded cluster topic terms and document set to well cluster document with respect to the appropriation cluster. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• Journal of Korea Trade
• /
• v.24 no.8
• /
• pp.171-188
• /
• 2020

Purpose - This is an exploratory study that aims to apply text mining techniques, which computationally extracts words from the large-scale text data, to legal documents to quantify trade claim contents and enables statistical analysis. Design/methodology - This is designed to verify the validity of the application of text mining techniques as a quantitative methodology for trade claim studies, that have relied mainly on a qualitative approach. The subjects are 81 cases of arbitration and court judgments from China published on the website of the UNCITRAL where the CISG was applied. Validation is performed by comparing the manually analyzed result with the automatically analyzed result. The manual analysis result is the cluster analysis wherein the researcher reads and codes the case. The automatic analysis result is an analysis applying text mining techniques to the result of the cluster analysis. Topic modeling and semantic network analysis are applied for the statistical approach. Findings - Results show that the results of cluster analysis and text mining results are consistent with each other and the internal validity is confirmed. And the degree centrality of words that play a key role in the topic is high as the between centrality of words that are useful for grasping the topic and the eigenvector centrality of the important words in the topic is high. This indicates that text mining techniques can be applied to research on content analysis of trade claims for statistical analysis. Originality/value - Firstly, the validity of the text mining technique in the study of trade claim cases is confirmed. Prior studies on trade claims have relied on traditional approach. Secondly, this study has an originality in that it is an attempt to quantitatively study the trade claim cases, whereas prior trade claim cases were mainly studied via qualitative methods. Lastly, this study shows that the use of the text mining can lower the barrier for acquiring information from a large amount of digitalized text.

• Journal of Korean Society for Quality Management
• /
• v.44 no.2
• /
• pp.389-408
• /
• 2016

Purpose: This study was designed to analyze the behavioral change of knowledge structures and the trends of research topics in the quality management field. Methods: The network structure and knowledge structure of the words were visualized in map form using co-word analysis, cluster analysis and strategic diagram. Results: Summarizing the research results obtained in this study are as follows. First, the word network derived from co-occurrence matrix had 106 nodes and 5,314 links and its density was analyzed to 0.95. Average betweenness centrality of word network was 2.37. In addition, average closeness centrality and average eigenvector centrality of word network were 0.01. Second, by applying optimal criteria of cluster decision and K-means algorithm to word co-occurrence matrix, 106 words were grouped into seven clusters such as standard & efficiency, product design, reliability, control chart, quality model, 6 sigma, and service quality. Conclusion: According to the results of strategic diagram analysis over time, the traditional research topics of quality management field related to reliability, 6 sigma, control chart topics in the third quadrant were revealed to be declined for their study importance. Research topics related to product design and customer satisfaction were found to be an important research topic over analysis periods. Research topic related to management innovation was emerging state and the scope of research topics related to process model was extended to research topics with system performance. Research topic related to service quality located in the first quadrant was analyzed as the key research topic.

• Journal of KIISE
• /
• v.41 no.9
• /
• pp.652-665
• /
• 2014

Many important terminologies in biomedical text are expressed as abbreviations or acronyms. We newly suggest a semantic link topic model based on the concepts of topic and dependency link to disambiguate biomedical abbreviations and cluster long form variants of abbreviations which refer to the same senses. This model is a generative model inspired by the latent Dirichlet allocation (LDA) topic model, in which each document is viewed as a mixture of topics, with each topic characterized by a distribution over words. Thus, words of a document are generated from a hidden topic structure of a document and the topic structure is inferred from observable word sequences of document collections. In this study, we allow two distinct word generation to incorporate semantic dependencies between words, particularly between expansions (long forms) of abbreviations and their sentential co-occurring words. Besides topic information, the semantic dependency between words is defined as a link and a new random parameter for the link presence is assigned to each word. As a result, the most probable expansions with respect to abbreviations of a given abstract are decided by word-topic distribution, document-topic distribution, and word-link distribution estimated from document collection though the semantic dependency link topic model. The abstracts retrieved from the MEDLINE Entrez interface by the query relating 22 abbreviations and their 186 expansions were used as a data set. The link topic model correctly predicted expansions of abbreviations with the accuracy of 98.30%.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.26 no.4
• /
• pp.245-262
• /
• 2015

As the topics of general education in universities become more diverse, it is not an easy task to identify the topics of general education courses. This study aims to identify and visualize the topics of A university's general education courses using informetric analysis methods. 214 syllabi were collected and titles, course introduction, goals, and weekly plans were analyzed. 278 topic words were extracted from the data set and grouped into 8 clusters. In the network analysis, topic clusters were divided into two areas, personal and social. Personal area has 14 sub-topic clusters and social area has 11 sub-topic clusters. In personal area, 'language', 'science', and 'personality' were major topic clusters. In social area, 'multi-culture' cluster was the core cluster with connected to four other clusters. The topic network generated in this study can be used for the university and the university library to enhance general education or to develop collections for general education.

• Journal of Information Science Theory and Practice
• /
• v.2 no.4
• /
• pp.31-48
• /
• 2014

The proliferation of many interactive Topic Detection and Tracking (iTDT) systems has motivated researchers to design systems that can track and detect news better. iTDT focuses on user interaction, user evaluation, and user interfaces. Recently, increasing effort has been devoted to user interfaces to improve TDT systems by investigating not just the user interaction aspect but also user and task oriented evaluation. This study investigates the combination of the bag of words and named entities approaches implemented in the iTDT interface, called Interactive Event Tracking (iEvent), including what TDT tasks these approaches facilitate. iEvent is composed of three components, which are Cluster View (CV), Document View (DV), and Term View (TV). User experiments have been carried out amongst journalists to compare three settings of iEvent: Setup 1 and Setup 2 (baseline setups), and Setup 3 (experimental setup). Setup 1 used bag of words and Setup 2 used named entities, while Setup 3 used a combination of bag of words and named entities. Journalists were asked to perform TDT tasks: Tracking and Detection. Findings revealed that the combination of bag of words and named entities approaches generally facilitated the journalists to perform well in the TDT tasks. This study has confirmed that the combination approach in iTDT is useful and enhanced the effectiveness of users' performance in performing the TDT tasks. It gives suggestions on the features with their approaches which facilitated the journalists in performing the TDT tasks.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.2
• /
• pp.45-52
• /
• 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 Korea Information Processing Society Conference
• /
• 2011.04a
• /
• pp.1538-1541
• /
• 2011

본 논문은 군집 주제의 유의어와 유사도를 이용하여 문서군집의 성능을 향상시키는 방법을 제안한다. 제안된 방법은 비음수행렬분해의 의미특징을 이용하여 군집 주제(topic)의 용어들을 선택함으로서 문서 군집 집합의 내부구조를 잘 표현할 수 있으며, 군집 주제의 용어들에 워드넷의 유의어를 사용하여서 확장함으로써 문서를 용어집합(bag-of-words)으로 표현하는 문제를 해결할 수 있다. 또한 확장된 군집 주제의 용어와 문서집합에 코사인 유사도를 이용하여서 군집의 주제에 적합한 문서를 잘 군집하여서 성능을 높일 수 있다. 실험결과 제안방법을 적용한 문서군집방법이 다른 문서군집 방법에 비하여 좋은 성능을 보인다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.10a
• /
• pp.392-393
• /
• 2012

This paper proposes a new document clustering method using clustering and Wikipedia. The proposed method can well represent the concept of cluster topics by means 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. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.