• Journal of Computing Science and Engineering
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• 제2권2호
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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.

• 한국멀티미디어학회논문지
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• 제11권12호
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• pp.1749-1757
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• 2008

This research proposes a modified version of single pass algorithm specialized for text clustering. Encoding documents into numerical vectors for using the traditional version of single pass algorithm causes the two main problems: huge dimensionality and sparse distribution. Therefore, in order to address the two problems, this research modifies the single pass algorithm into its version where documents are encoded into not numerical vectors but other forms. In the proposed version, documents are mapped into tables and the operation on two tables is defined for using the single pass algorithm. The goal of this research is to improve the performance of single pass algorithm for text clustering by modifying it into the specialized version.

• Journal of information and communication convergence engineering
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• 제4권4호
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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.

• 스마트미디어저널
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• 제1권4호
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• pp.27-34
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• 2012

In this paper, a novel text binarization is presented that can deal with some complex conditions, such as shadows, non-uniform illumination due to highlight or object projection, and messy backgrounds. To locate the target text region, a focus line is assumed to pass through a text region. Next, connected component analysis and stroke width estimation based on location information of the focus line is used to locate the bounding box of the text region, and each box of connected components. A series of classifications are applied to identify whether each CC(Connected component) is text or non-text. Also, a modified K-means clustering method based on an HCL color space is applied to reduce the color dimension. A text binarization procedure based on location of text component and seed color pixel is then used to generate the final result.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.231-237
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• 2008

This research proposes a modified version of single pass algorithm specialized for text clustering. Encoding documents into numerical vectors for using the traditional version of single pass algorithm causes the two main problems: huge dimensionality and sparse distribution. Therefore, in order to address the two problems, this research modifies the single pass algorithm into its version where documents are encoded into not numerical vectors but other forms. In the proposed version, documents are mapped into tables and the operation on two tables is defined for using the single pass algorithm. The goal of this research is to improve the performance of single pass algorithm for text clustering by modifying it into the specialized version.

• Journal of Information Technology Applications and Management
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• 제22권3호
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• pp.83-103
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• 2015

The rapid development of internet technologies and social media over the last few years has generated a huge amount of unstructured text data, which contains a great deal of valuable information and issues. Therefore, text mining-extracting meaningful information from unstructured text data-has gained attention from many researchers in various fields. Topic analysis is a text mining application that is used to determine the main issues in a large volume of text documents. However, it is difficult to identify related issues or meaningful insights as the number of issues derived through topic analysis is too large. Furthermore, traditional issue-clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be recognized using traditional issue-clustering methods, even if those issues are strongly related in other perspectives. Therefore, in this research, a methodology to reorganize social issues from a research and development (R&D) perspective using social network analysis is proposed. Using an R&D perspective lexicon, issues that consistently share the same R&D keywords can be further identified through social network analysis. In this study, the R&D keywords that are associated with a particular issue imply the key technology elements that are needed to solve a particular issue. Issue clustering can then be performed based on the analysis results. Furthermore, the relationship between issues that share the same R&D keywords can be reorganized more systematically, by grouping them into clusters according to the R&D perspective lexicon. We expect that our methodology will contribute to establishing efficient R&D investment policies at the national level by enhancing the reusability of R&D knowledge, based on issue clustering using the R&D perspective lexicon. In addition, business companies could also utilize the results by aligning the R&D with their business strategy plans, to help companies develop innovative products and new technologies that sustain innovative business models.

• 정보처리학회논문지B
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• 제13B권3호
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• pp.205-214
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• 2006

이미지에 포함된 텍스트는 이미지의 내용을 함축적이고 구체적으로 표현하는 정보로서 이러한 정보를 실시간에 찾아내서 인식한다면 다양한 응용에 활용할 수 있다. 본 논문에서는 카메라로 취득한 다양한 종류의 이미지로부터 텍스트를 추출하는 방법과 추출된 영역에서 텍스트를 분리하는 방법을 새롭게 제안한다. 텍스트 영역 추출을 위해서 RGB 색 공간에서 색 분산을 특징으로 제안하며, 텍스트 영역 분리를 위해서 RGB 색 공간에서 개선된 K-means 병합을 제안한다. 실험은 디지털 카메라와 핸드폰 카메라로 취득한 다양한 종류의 문서유형 이미지와 실내외의 일반적인 자연이미지를 사용하였으며, ICDAR 콘테스트[1] 이미지의 일부도 사용하였다.

• 대한산업공학회지
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• 제41권5호
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• pp.453-460
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• 2015

Identification of meaningful patterns and trends in large volumes of text data is an important task in various research areas. In the present study, we propose a procedure to find meaningful tendencies based on a combination of text mining, cluster analysis, and low-dimensional embedding. To demonstrate applicability and effectiveness of the proposed procedure, we analyzed the inaugural addresses of the presidents of the United States from 1789 to 2009. The main results of this study show that trends in the national policy agenda can be discovered based on clustering and visualization algorithms.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.396-404
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• 2022

Online social networks contain a large amount of data that can be converted into valuable and insightful information. Text mining approaches allow exploring large-scale data efficiently. Therefore, this study reviews the recent literature on text mining in online social networks in a way that produces valid and valuable knowledge for further research. The review identifies text mining techniques used in social networking, the data used, tools, and the challenges. Research questions were formulated, then search strategy and selection criteria were defined, followed by the analysis of each paper to extract the data relevant to the research questions. The result shows that the most social media platforms used as a source of the data are Twitter and Facebook. The most common text mining technique were sentiment analysis and topic modeling. Classification and clustering were the most common approaches applied by the studies. The challenges include the need for processing with huge volumes of data, the noise, and the dynamic of the data. The study explores the recent development in text mining approaches in social networking by providing state and general view of work done in this research area.

• 한국음향학회지
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• 제22권8호
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• pp.703-710
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• 2003

코퍼스 기반 음성합성방식은 그 합성음의 자연성이 매우 우수하여 널리 사용되고 있으나 대용량의 데이터베이스 (DB)를 사용하기 때문에 그 적용분야가 매우 제한적이다. 본 연구에서는 이러한 코퍼스 기반 음성합성기의 대용량 DB 문제를 해결하기 위한 방안으로서 DB 축소 방법 대한 알고리듬을 제안하고 평가하였다. 본 논문에서는 DB 축소 알고리듬으로서 세 가지 방법을 제안하였는데, 첫 번째는 Modified K-means 군집화를 이용한 DB 축소 알고리듬이고 다음은 적절한 문장 셋을 정의하고 이 문장 셋을 합성할 때 사용된 단위들을 이용하는 방법이다. 마지막으로는 대용량 문장 셋을 정의하고 해당 문장을 음성합성하고, 음편들의 사용 빈도수를 고려하여 군집화를 하는 것이다. 세 가지 방법을 이용하여 합성 DB를 유사한 크기로 축소하였을 때, 대용량 문장 셋과 빈도를 고려한 세 번째 방법이 가장 우수한 음질을 보였다. 또한 마지막 방법은 합성음의 음질은 저하시키지 않으면서 합성 DB만을 감소시키는 성능을 보여, 제안된 방법의 타당함을 입증할 수 있었다.