-
Berry, M.W.; Esau, R.; Kiefer, B.: ¬The use of text mining techniques in electronic discovery for legal matters (2012)
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- Abstract
- Electronic discovery (eDiscovery) is the process of collecting and analyzing electronic documents to determine their relevance to a legal matter. Office technology has advanced and eased the requirements necessary to create a document. As such, the volume of data has outgrown the manual processes previously used to make relevance judgments. Methods of text mining and information retrieval have been put to use in eDiscovery to help tame the volume of data; however, the results have been uneven. This chapter looks at the historical bias of the collection process. The authors examine how tools like classifiers, latent semantic analysis, and non-negative matrix factorization deal with nuances of the collection process.
- Imprint
- Hershey, PA : IGI Publishing
-
Saz, J.T.: Perspectivas en recuperacion y explotacion de informacion electronica : el 'data mining' (1997)
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- Footnote
- Übers. des Titels: Perspectives on the retrieval and exploitation of electronic information: data mining
-
Chen, C.-C.; Chen, A.-P.: Using data mining technology to provide a recommendation service in the digital library (2007)
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- Abstract
- Purpose - Since library storage has been increasing day by day, it is difficult for readers to find the books which interest them as well as representative booklists. How to utilize meaningful information effectively to improve the service quality of the digital library appears to be very important. The purpose of this paper is to provide a recommendation system architecture to promote digital library services in electronic libraries. Design/methodology/approach - In the proposed architecture, a two-phase data mining process used by association rule and clustering methods is designed to generate a recommendation system. The process considers not only the relationship of a cluster of users but also the associations among the information accessed. Findings - The process considered not only the relationship of a cluster of users but also the associations among the information accessed. With the advanced filter, the recommendation supported by the proposed system architecture would be closely served to meet users' needs. Originality/value - This paper not only constructs a recommendation service for readers to search books from the web but takes the initiative in finding the most suitable books for readers as well. Furthermore, library managers are expected to purchase core and hot books from a limited budget to maintain and satisfy the requirements of readers along with promoting digital library services.
- Source
- Electronic library. 25(2007) no.6, S.711-724
-
Chowdhury, G.G.: Template mining for information extraction from digital documents (1999)
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- Date
- 2. 4.2000 18:01:22
-
Nicholson, S.: Bibliomining for automated collection development in a digital library setting : using data mining to discover Web-based scholarly research works (2003)
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- Abstract
- This research creates an intelligent agent for automated collection development in a digital library setting. It uses a predictive model based an facets of each Web page to select scholarly works. The criteria came from the academic library selection literature, and a Delphi study was used to refine the list to 41 criteria. A Perl program was designed to analyze a Web page for each criterion and applied to a large collection of scholarly and nonscholarly Web pages. Bibliomining, or data mining for libraries, was then used to create different classification models. Four techniques were used: logistic regression, nonparametric discriminant analysis, classification trees, and neural networks. Accuracy and return were used to judge the effectiveness of each model an test datasets. In addition, a set of problematic pages that were difficult to classify because of their similarity to scholarly research was gathered and classified using the models. The resulting models could be used in the selection process to automatically create a digital library of Webbased scholarly research works. In addition, the technique can be extended to create a digital library of any type of structured electronic information.
-
KDD : techniques and applications (1998)
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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
-
Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012)
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- Imprint
- Hershey, PA : IGI Publishing
-
Cohen, D.J.: From Babel to knowledge : data mining large digital collections (2006)
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- Abstract
- In Jorge Luis Borges's curious short story The Library of Babel, the narrator describes an endless collection of books stored from floor to ceiling in a labyrinth of countless hexagonal rooms. The pages of the library's books seem to contain random sequences of letters and spaces; occasionally a few intelligible words emerge in the sea of paper and ink. Nevertheless, readers diligently, and exasperatingly, scan the shelves for coherent passages. The narrator himself has wandered numerous rooms in search of enlightenment, but with resignation he simply awaits his death and burial - which Borges explains (with signature dark humor) consists of being tossed unceremoniously over the library's banister. Borges's nightmare, of course, is a cursed vision of the research methods of disciplines such as literature, history, and philosophy, where the careful reading of books, one after the other, is supposed to lead inexorably to knowledge and understanding. Computer scientists would approach Borges's library far differently. Employing the information theory that forms the basis for search engines and other computerized techniques for assessing in one fell swoop large masses of documents, they would quickly realize the collection's incoherence though sampling and statistical methods - and wisely start looking for the library's exit. These computational methods, which allow us to find patterns, determine relationships, categorize documents, and extract information from massive corpuses, will form the basis for new tools for research in the humanities and other disciplines in the coming decade. For the past three years I have been experimenting with how to provide such end-user tools - that is, tools that harness the power of vast electronic collections while hiding much of their complicated technical plumbing. In particular, I have made extensive use of the application programming interfaces (APIs) the leading search engines provide for programmers to query their databases directly (from server to server without using their web interfaces). In addition, I have explored how one might extract information from large digital collections, from the well-curated lexicographic database WordNet to the democratic (and poorly curated) online reference work Wikipedia. While processing these digital corpuses is currently an imperfect science, even now useful tools can be created by combining various collections and methods for searching and analyzing them. And more importantly, these nascent services suggest a future in which information can be gleaned from, and sense can be made out of, even imperfect digital libraries of enormous scale. A brief examination of two approaches to data mining large digital collections hints at this future, while also providing some lessons about how to get there.
-
Mining text data (2012)
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- Abstract
- Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.
-
Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997)
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- Date
- 22.11.1998 18:57:22
-
Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999)
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- Date
- 17. 7.2002 19:22:06
-
Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997)
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- Source
- Information systems. 22(1997) nos.5/6, S.333-347
-
Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997)
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- Source
- Information systems. 22(1997) nos.5/6, S.349-385
-
Lackes, R.; Tillmanns, C.: Data Mining für die Unternehmenspraxis : Entscheidungshilfen und Fallstudien mit führenden Softwarelösungen (2006)
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- Date
- 22. 3.2008 14:46:06
-
Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013)
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- Date
- 22. 3.2013 19:43:01
-
Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015)
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- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
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Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019)
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- Date
- 7. 3.2019 16:32:22
-
Peters, G.; Gaese, V.: ¬Das DocCat-System in der Textdokumentation von G+J (2003)
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- Date
- 22. 4.2003 11:45:36
-
Hölzig, C.: Google spürt Grippewellen auf : Die neue Anwendung ist bisher auf die USA beschränkt (2008)
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- Date
- 3. 5.1997 8:44:22
-
Jäger, L.: Von Big Data zu Big Brother (2018)
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- Date
- 22. 1.2018 11:33:49
