Search (36 results, page 1 of 2)

  • × theme_ss:"Data Mining"
  1. Miao, Q.; Li, Q.; Zeng, D.: Fine-grained opinion mining by integrating multiple review sources (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.11, S.2288-2299
    Year
    2010
  2. Thelwall, M.; Wilkinson, D.: Public dialogs in social network sites : What is their purpose? (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.2, S.392-404
    Year
    2010
  3. Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119
    Year
    2010
  4. Leydesdorff, L.; Persson, O.: Mapping the geography of science : distribution patterns and networks of relations among cities and institutes (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1622-1634
    Year
    2010
  5. Berendt, B.; Krause, B.; Kolbe-Nusser, S.: Intelligent scientific authoring tools : interactive data mining for constructive uses of citation networks (2010) 0.00 
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    Source
    Information processing and management. 46(2010) no.1, S.1-10
    Year
    2010
  6. Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010) 0.00 
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    Year
    2010
  7. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.00 
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    Date
    2. 4.2000 18:01:22
  8. Wong, M.L.; Leung, K.S.; Cheng, J.C.Y.: Discovering knowledge from noisy databases using genetic programming (2000) 0.00 
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    Abstract
    In data mining, we emphasize the need for learning from huge, incomplete, and imperfect data sets. To handle noise in the problem domain, existing learning systems avoid overfitting the imperfect training examples by excluding insignificant patterns. The problem is that these systems use a limiting attribute-value language for representing the training examples and the induced knowledge. Moreover, some important patterns are ignored because they are statistically insignificant. In this article, we present a framework that combines genetic programming and inductive logic programming to induce knowledge represented in various knowledge representation formalisms from noisy databases (LOGENPRO). Moreover, the system is applied to one real-life medical database. The knowledge discovered provides insights to and allows better understanding of the medical domains
  9. KDD : techniques and applications (1998) 0.00 
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    Footnote
    A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997
  10. Fayyad, U.M.; Djorgovski, S.G.; Weir, N.: From digitized images to online catalogs : data ming a sky server (1996) 0.00 
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    Abstract
    Offers a data mining approach based on machine learning classification methods to the problem of automated cataloguing of online databases of digital images resulting from sky surveys. The SKICAT system automates the reduction and analysis of 3 terabytes of images expected to contain about 2 billion sky objects. It offers a solution to problems associated with the analysis of large data sets in science
  11. Thelwall, M.; Wilkinson, D.; Uppal, S.: Data mining emotion in social network communication : gender differences in MySpace (2009) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.190-199
  12. Kong, S.; Ye, F.; Feng, L.; Zhao, Z.: Towards the prediction problems of bursting hashtags on Twitter (2015) 0.00 
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    Abstract
    Hundreds of thousands of hashtags are generated every day on Twitter. Only a few will burst and become trending topics. In this article, we provide the definition of a bursting hashtag and conduct a systematic study of a series of challenging prediction problems that span the entire life cycles of bursting hashtags. Around the problem of "how to build a system to predict bursting hashtags," we explore different types of features and present machine learning solutions. On real data sets from Twitter, experiments are conducted to evaluate the effectiveness of the proposed solutions and the contributions of features.
  13. Deogun, J.S.: Feature selection and effective classifiers (1998) 0.00 
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    Abstract
    Develops and analyzes 4 algorithms for feature selection in the context of rough set methodology. Develops the notion of accuracy of classification that can be used for upper or lower classification methods and defines the feature selection problem. Presents a discussion of upper classifiers and develops 4 features selection heuristics and discusses the family of stepwise backward selection algorithms. Analyzes the worst case time complexity in all algorithms presented. Discusses details of the experiments and results of using a family of stepwise backward selection learning data sets and a duodenal ulcer data set. Includes the experimental setup and results of comparison of lower classifiers and upper classiers on the duodenal ulcer data set. Discusses exteded decision tables
  14. Chen, H.; Chau, M.: Web mining : machine learning for Web applications (2003) 0.00 
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    Abstract
    With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. Analysis of these characteristics often reveals interesting patterns and new knowledge. Such knowledge can be used to improve users' efficiency and effectiveness in searching for information an the Web, and also for applications unrelated to the Web, such as support for decision making or business management. The Web's size and its unstructured and dynamic content, as well as its multilingual nature, make the extraction of useful knowledge a challenging research problem. Furthermore, the Web generates a large amount of data in other formats that contain valuable information. For example, Web server logs' information about user access patterns can be used for information personalization or improving Web page design.
  15. Dang, X.H.; Ong. K.-L.: Knowledge discovery in data streams (2009) 0.00 
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    Abstract
    Knowing what to do with the massive amount of data collected has always been an ongoing issue for many organizations. While data mining has been touted to be the solution, it has failed to deliver the impact despite its successes in many areas. One reason is that data mining algorithms were not designed for the real world, i.e., they usually assume a static view of the data and a stable execution environment where resourcesare abundant. The reality however is that data are constantly changing and the execution environment is dynamic. Hence, it becomes difficult for data mining to truly deliver timely and relevant results. Recently, the processing of stream data has received many attention. What is interesting is that the methodology to design stream-based algorithms may well be the solution to the above problem. In this entry, we discuss this issue and present an overview of recent works.
  16. Chen, Y.-L.; Liu, Y.-H.; Ho, W.-L.: ¬A text mining approach to assist the general public in the retrieval of legal documents (2013) 0.00 
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    Abstract
    Applying text mining techniques to legal issues has been an emerging research topic in recent years. Although some previous studies focused on assisting professionals in the retrieval of related legal documents, they did not take into account the general public and their difficulty in describing legal problems in professional legal terms. Because this problem has not been addressed by previous research, this study aims to design a text-mining-based method that allows the general public to use everyday vocabulary to search for and retrieve criminal judgments. The experimental results indicate that our method can help the general public, who are not familiar with professional legal terms, to acquire relevant criminal judgments more accurately and effectively.
  17. Qiu, X.Y.; Srinivasan, P.; Hu, Y.: Supervised learning models to predict firm performance with annual reports : an empirical study (2014) 0.00 
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    Abstract
    Text mining and machine learning methodologies have been applied toward knowledge discovery in several domains, such as biomedicine and business. Interestingly, in the business domain, the text mining and machine learning community has minimally explored company annual reports with their mandatory disclosures. In this study, we explore the question "How can annual reports be used to predict change in company performance from one year to the next?" from a text mining perspective. Our article contributes a systematic study of the potential of company mandatory disclosures using a computational viewpoint in the following aspects: (a) We characterize our research problem along distinct dimensions to gain a reasonably comprehensive understanding of the capacity of supervised learning methods in predicting change in company performance using annual reports, and (b) our findings from unbiased systematic experiments provide further evidence about the economic incentives faced by analysts in their stock recommendations and speculations on analysts having access to more information in producing earnings forecast.
  18. Sarnikar, S.; Zhang, Z.; Zhao, J.L.: Query-performance prediction for effective query routing in domain-specific repositories (2014) 0.00 
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    Abstract
    The effective use of corporate memory is becoming increasingly important because every aspect of e-business requires access to information repositories. Unfortunately, less-than-satisfying effectiveness in state-of-the-art information-retrieval techniques is well known, even for some of the best search engines such as Google. In this study, the authors resolve this retrieval ineffectiveness problem by developing a new framework for predicting query performance, which is the first step toward better retrieval effectiveness. Specifically, they examine the relationship between query performance and query context. A query context consists of the query itself, the document collection, and the interaction between the two. The authors first analyze the characteristics of query context and develop various features for predicting query performance. Then, they propose a context-sensitive model for predicting query performance based on the characteristics of the query and the document collection. Finally, they validate this model with respect to five real-world collections of documents and demonstrate its utility in routing queries to the correct repository with high accuracy.
  19. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.00 
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    Date
    22.11.1998 18:57:22
  20. Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999) 0.00 
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    Date
    17. 7.2002 19:22:06

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