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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

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Now showing 1 - 6 of 6
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    Semi-supervised Linear Discriminant Clustering
    (2014-07-01)
    Liu, Chien-Liang
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    Hsaio, Wen-Hoar
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    Lee, Chia-Hoang
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    Gou, Fu-Sheng
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    交大名義發表
    This paper devises a semi-supervised learning method called semi-supervised linear discriminant clustering (Semi-LDC). The proposed algorithm considers clustering and dimensionality reduction simultaneously by connecting K-means and linear discriminant analysis (LDA). The goal is to find a feature space where the K-means can perform well in the new space. To exploit the information brought by unlabeled examples, this paper proposes to use soft labels to denote the labels of unlabeled examples. The Semi-LDC uses the proposed algorithm, called constrained-PLSA, to estimate the soft labels of unlabeled examples. We use soft LDA with hard labels of labeled examples and soft labels of unlabeled examples to find a projection matrix. The clustering is then performed in the new feature space. We conduct experiments on three data sets. The experimental results indicate that the proposed method can generally outperform other semi-supervised methods. We further discuss and analyze the influence of soft labels on classification performance by conducting experiments with different percentages of labeled examples. The finding shows that using soft labels can improve performance particularly when the number of available labeled examples is insufficient to train a robust and accurate model. Additionally, the proposed method can be viewed as a framework, since different soft label estimation methods can be used in the proposed method according to application requirements.
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    Knowledge Discovery of Service Satisfaction Based on Text Analysis of Critical Incident Dialogues and Clustering Methods
    (2013)
    Trappey, Charles
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    Wu, Hsin-Ying
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    Liu, Kuan-Liang
    ;
    Lin, Feng-Teng
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    管理科學系
    Text mining of consumer's dialogues regarding their service experiences provides a direct and unbiased feedback to service providers. This research proposes an analysis process to analyze unstructured input from consumer dialogues. The goal is to apply the critical incident and text mining methods to discover factors that contribute to customer satisfaction and dissatisfaction. The critical incident method is used to construct an open-ended questionnaire to collect customer's positive and negative opinions toward the service provided. Valid and reliable text mining techniques are used to cluster significant text to help analyze incidents that customers care about. A case study of consumers riding the Kaohsiung Mass Rapid Transit System (KMRT) was cased to evaluate the proposed analysis process. Based on dialogues collected from the open-ended questionnaires, the analysis process extracts key phrases related to consumer's best and worst service experiences, creates significant dialogue clusters, and derives meaningful trends, baselines, and interpretations of consumer satisfaction and dissatisfaction. The results of this case study can be used as a basis for building more complete analytical methods to understand consumer experiences and provide strategic feedback for service providers.
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    Movie Rating and Review Summarization in Mobile Environment
    (2012-05-01)
    Liu, Chien-Liang
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    Hsaio, Wen-Hoar
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    Lee, Chia-Hoang
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    Lu, Gen-Chi
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    Jou, Emery
    In this paper, we design and develop a movie-rating and review-summarization system in a mobile environment. The movie-rating information is based on the sentiment-classification result. The condensed descriptions of movie reviews are generated from the feature-based summarization. We propose a novel approach based on latent semantic analysis (LSA) to identify product features. Furthermore, we find away to reduce the size of summary based on the product features obtained from LSA. We consider both sentiment-classification accuracy and system response time to design the system. The rating and review-summarization system can be extended to other product-review domains easily.
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    Knowledge support for problem-solving in a production process: A hybrid of knowledge discovery and case-based reasoning
    (2007-07-01)
    Liu, Duen-Ren
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    Ke, Chih-Kun
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    ;
    Department of Information Management and Finance
    Problem-solving is an important process that enables corporations to create competitive business advantages. Traditionally, case-based reasoning techniques have been widely used to help workers solve problems. However, conventional approaches focus on identifying similar problems without exploring the information needs of workers during the problem-solving process. Such processes are usually knowledge intensive tasks; therefore, workers need effective knowledge support that gives them the information necessary to identify the causes of a problem and enables them to take appropriate action to resolve the situation. In this paper, we propose a mining-based knowledge support system for problem-solving. In addition to adopting case-based reasoning to identify similar situations and the action taken to solve them, the proposed system employs text mining (information retrieval) techniques to extract the key concepts of situations and actions. These concepts form profiles that model workers' information needs when handling problems. Effective knowledge support can thus be facilitated by providing workers with situation/action-relevant information based on the profiles. Moreover, association rule mining is used to discover hidden knowledge patterns from historical problem-solving logs. The discovered patterns identify frequent associations between situations and actions, and can therefore provide decision-making knowledge, i.e., appropriate actions for handling specific situations. We develop a prototype system to demonstrate the effectiveness of providing situation/action relevant information and decision-making knowledge to help workers solve problems. (c) 2006 Published by Elsevier Ltd.
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    AUTOMATIC PATENT DOCUMENT SUMMARIZATION FOR COLLABORATIVE KNOWLEDGE SYSTEMS AND SERVICES
    (2009-03-01)
    Trappey, Amy J. C.
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    Trappey, Charles V.
    ;
    Wu, Chun-Yi
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    管理科學系
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    Department of Management Science
    Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R&D knowledge, reduces new product development time, increases market success, and reduces potential patent infringement. Thus, it is beneficial to automatically and systematically extract information from patent documents in order to improve knowledge sharing and collaboration among R&D team members. In this research, patents are summarized using a combined ontology based and TF-IDF concept clustering approach. The ontology captures the general knowledge and core meaning of patents in a given domain. Then, the proposed methodology extracts, clusters, and integrates the content of a patent to derive a summary and a cluster tree diagram of key terms. Patents from the International Patent Classification (IPC) codes B25C, B25D, B25F (categories for power hand tools) and B24B, C09G and H011 (categories for chemical mechanical polishing) are used as case studies to evaluate the compression ratio, retention ratio, and classification accuracy of the summarization results. The evaluation uses statistics to represent the summary generation and its compression ratio, the ontology based keyword extraction retention ratio, and the summary classification accuracy. The results show that the ontology based approach yields about the same compression ratio as previous non-ontology based research but yields on average an 11% improvement for the retention ratio and a 14% improvement for classification accuracy.
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    A Fuzzy Ontological Knowledge Document Clustering Methodology
    (2009-06-01)
    Trappey, Amy J. C.
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    Trappey, Charles V.
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    Hsu, Fu-Chiang
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    Hsiao, David W.
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    管理科學系
    This correspondence presents a novel hierarchical clustering approach for knowledge document self-organization, particularly for patent analysis. Current keyword-based methodologies for document content management tend to be inconsistent and ineffective when partial meanings of the technical content are used for cluster analysis. Thus, a new methodology to automatically interpret and cluster knowledge documents using an ontology schema is presented. Moreover, a fuzzy logic control approach is used to match suitable document cluster(s) for given patents based on their derived ontological semantic webs. Finally, three case studies are used to test the approach. The first test case analyzed and clustered 100 patents for chemical and mechanical polishing retrieved from the World Intellectual Property Organization (WIPO). The second test case analyzed. and clustered 100 patent news articles retrieved from online Web sites. The third case analyzed and clustered 100 patents for radio-frequency identification retrieved from WIPO. The results show that the fuzzy ontology-based document clustering approach outperforms the K-means approach in precision, recall, F-measure, and Shannon's entropy.