Search (4020 results, page 2 of 201)

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Abstract

Gives an overview of data mining and knowledge discovery in databases. Clarifies how they are related both to each other and to related fields. Mentions real world applications data mining techniques, challenges involved in real world applications of knowledge discovery, and current and future research directions

Theme

Data Mining

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Theme

Data Mining

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Theme

Data Mining

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Theme

Data Mining

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Theme

Data Mining

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Theme

Data Mining

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Theme

Data Mining

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Abstract

As with print collections, the evaluation and selection of CD-ROMs should be based on established guidelines. Such attributes as computer network compatibility and platform are exclusively applicable to CD-ROM. Presents a knowledge based system to prioritize and select CD-ROMs for a library collection, operating on consistency driven pairwise comparisons. The computer system indicates the most inconsistent judgements and allows librarians to reconsider their position. After consistency analysis is completed, the software computes the weights of all criteria used in the evaluation process. The system includes a subsystem for evaluating CD-ROM titles. Offers a CD-ROM evaluation form. Discusses cost considerations; the use of pairwise comparisons in knowledge based systems with reference to data mining; the CD-ROM selection process; and consistency analysis of experts' judgements

Date

6. 3.1997 16:22:15

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Source

Information systems. 22(1997) nos.5/6, S.349-385

Theme

Data Mining

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Footnote

Contribution to 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

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Abstract

Das Handbuch der Künstlichen Intelligenz bietet die umfassendste deutschsprachige Übersicht über die Disziplin "Künstliche Intelligenz". Es vereinigt einführende und weiterführende Beiträge u.a. zu folgenden Themen: - Kognition - Neuronale Netze - Suche, Constraints - Wissensrepräsentation - Logik und automatisches Beweisen - Unsicheres und vages Wissen - Wissen über Raum und Zeit - Fallbasiertes Schließen und modellbasierte Systeme - Planen - Maschinelles Lernen und Data Mining - Sprachverarbeitung - Bildverstehen - Robotik - Software-Agenten Das Handbuch bietet eine moderne Einführung in die Künstliche Intelligenz und zugleich einen aktuellen Überblick über Theorien, Methoden und Anwendungen.

Date

21. 3.2008 19:10:22

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Abstract

What is text mining, and how can it be used? What relevance do these methods have to everyday work in information science and the digital humanities? How does one develop competences in text mining? Working with Text provides a series of cross-disciplinary perspectives on text mining and its applications. As text mining raises legal and ethical issues, the legal background of text mining and the responsibilities of the engineer are discussed in this book. Chapters provide an introduction to the use of the popular GATE text mining package with data drawn from social media, the use of text mining to support semantic search, the development of an authority system to support content tagging, and recent techniques in automatic language evaluation. Focused studies describe text mining on historical texts, automated indexing using constrained vocabularies, and the use of natural language processing to explore the climate science literature. Interviews are included that offer a glimpse into the real-life experience of working within commercial and academic text mining.

LCSH

Data mining

RSWK

Text Mining / Aufsatzsammlung

Subject

Text Mining / Aufsatzsammlung
Data mining

Theme

Data Mining

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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.

Content

Inhalt: An Introduction to Text Mining.- Information Extraction from Text.- A Survey of Text Summarization Techniques.- A Survey of Text Clustering Algorithms.- Dimensionality Reduction and Topic Modeling.- A Survey of Text Classification Algorithms.- Transfer Learning for Text Mining.- Probabilistic Models for Text Mining.- Mining Text Streams.- Translingual Mining from Text Data.- Text Mining in Multimedia.- Text Analytics in Social Media.- A Survey of Opinion Mining and Sentiment Analysis.- Biomedical Text Mining: A Survey of Recent Progress.- Index.

LCSH

Data mining

RSWK

Text Mining / Aufsatzsammlung

Subject

Text Mining / Aufsatzsammlung
Data mining

Theme

Data Mining

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Abstract

Data Mining describes a technology that discovers non-trivial hidden patterns in a large collection of data. Although this technology has a tremendous impact on our lives, the invaluable contributions of this invisible technology often go unnoticed. This paper discusses advances in data mining while focusing on the emerging data mining capability. Such data mining applications perform multidimensional mining on a wide variety of heterogeneous data sources, providing solutions to many unresolved problems. This paper also highlights the advantages and disadvantages arising from the ever-expanding scope of data mining. Data Mining augments human intelligence by equipping us with a wealth of knowledge and by empowering us to perform our daily tasks better. As the mining scope and capacity increases, users and organizations become more willing to compromise privacy. The huge data stores of the 'master miners' allow them to gain deep insights into individual lifestyles and their social and behavioural patterns. Data integration and analysis capability of combining business and financial trends together with the ability to deterministically track market changes will drastically affect our lives.

Theme

Data Mining

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Content

1. Agrarwissenschaften 1981. - 3. Allgemeine Geographie 2.1972. - 3a. Allgemeine Naturwissenschaften 1.1973. - 4. Allgemeine Sprachwissenschaft, Allgemeine Literaturwissenschaft 2.1971. - 6. Allgemeines. 5.1983. - 7. Anglistik 3.1976. - 8. Astronomie, Geodäsie 4.1977. - 12. bio Biologie, bcp Biochemie-Biophysik, bot Botanik, zoo Zoologie 1981. - 13. Bremensien 3.1983. - 13a. Buch- und Bibliothekswesen 3.1975. - 14. Chemie 4.1977. - 14a. Elektrotechnik 1974. - 15 Ethnologie 2.1976. - 16,1. Geowissenschaften. Sachteil 3.1977. - 16,2. Geowissenschaften. Regionaler Teil 3.1977. - 17. Germanistik 6.1984. - 17a,1. Geschichte. Teilsystematik hil. - 17a,2. Geschichte. Teilsystematik his Neuere Geschichte. - 17a,3. Geschichte. Teilsystematik hit Neueste Geschichte. - 18. Humanbiologie 2.1983. - 19. Ingenieurwissenschaften 1974. - 20. siehe 14a. - 21. klassische Philologie 3.1977. - 22. Klinische Medizin 1975. - 23. Kunstgeschichte 2.1971. - 24. Kybernetik. 2.1975. - 25. Mathematik 3.1974. - 26. Medizin 1976. - 26a. Militärwissenschaft 1985. - 27. Musikwissenschaft 1978. - 27a. Noten 2.1974. - 28. Ozeanographie 3.1977. -29. Pädagogik 8.1985. - 30. Philosphie 3.1974. - 31. Physik 3.1974. - 33. Politik, Politische Wissenschaft, Sozialwissenschaft. Soziologie. Länderschlüssel. Register 1981. - 34. Psychologie 2.1972. - 35. Publizistik und Kommunikationswissenschaft 1985. - 36. Rechtswissenschaften 1986. - 37. Regionale Geograpgie 3.1975. - 37a. Religionswissenschaft 1970. - 38. Romanistik 3.1976. - 39. Skandinavistik 4.1985. - 40. Slavistik 1977. - 40a. Sonstige Sprachen und Literaturen 1973. - 43. Sport 4.1983. - 44. Theaterwissenschaft 1985. - 45. Theologie 2.1976. - 45a. Ur- und Frühgeschichte, Archäologie 1970. - 47. Volkskunde 1976. - 47a. Wirtschaftswissenschaften 1971 // Schlüssel: 1. Länderschlüssel 1971. - 2. Formenschlüssel (Kurzform) 1974. - 3. Personenschlüssel Literatur 5. Fassung 1968

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Abstract

Defines knowledge discovery and database mining. The challenge for knowledge discovery in databases (KDD) is to automatically process large quantities of raw data, identifying the most significant and meaningful patterns, and present these as as knowledge appropriate for achieving a user's goals. Data mining is the process of deriving useful knowledge from real world databases through the application of pattern extraction techniques. Explains the goals of, and motivation for, research work on data mining. Discusses the nature of database contents, along with problems within the field of data mining

Footnote

Contribution to a special issue devoted to knowledge discovery and data mining

Theme

Data Mining

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Abstract

Polarity mining provides an in-depth analysis of semantic orientations of text information. Motivated by its success in the area of topic mining, we propose an ontology-supported polarity mining (OSPM) approach. The approach aims to enhance polarity mining with ontology by providing detailed topic-specific information. OSPM was evaluated in the movie review domain using both supervised and unsupervised techniques. Results revealed that OSPM outperformed the baseline method without ontology support. The findings of this study not only advance the state of polarity mining research but also shed light on future research directions.

Theme

Data Mining

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Content

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Abstract

We present CopeOpi, an opinion-analysis system, which extracts from the Web opinions about specific targets, summarizes the polarity and strength of these opinions, and tracks opinion variations over time. Objects that yield similar opinion tendencies over a certain time period may be correlated due to the latent causal events. CopeOpi discovers relationships among objects based on their opinion-tracking plots and collocations. Event bursts are detected from the tracking plots, and the strength of opinion relationships is determined by the coverage of these plots. To evaluate opinion mining, we use the NTCIR corpus annotated with opinion information at sentence and document levels. CopeOpi achieves sentence- and document-level f-measures of 62% and 74%. For relationship discovery, we collected 1.3M economics-related documents from 93 Web sources over 22 months, and analyzed collocation-based, opinion-based, and hybrid models. We consider as correlated company pairs that demonstrate similar stock-price variations, and selected these as the gold standard for evaluation. Results show that opinion-based and collocation-based models complement each other, and that integrated models perform the best. The top 25, 50, and 100 pairs discovered achieve precision rates of 1, 0.92, and 0.79, respectively.

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Source

Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

Theme

Data Mining