-
Chowdhury, G.G.: Template mining for information extraction from digital documents (1999)
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- Date
- 2. 4.2000 18:01:22
- Theme
- Data Mining
-
Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997)
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- Abstract
- Defines digital libraries and discusses the effects of new technology on librarians. Examines the different viewpoints of librarians and information technologists on digital libraries. Describes the development of a digital library at the National Drug Intelligence Center, USA, which was carried out in collaboration with information technology experts. The system is based on Web enabled search technology to find information, data visualization and data mining to visualize it and use of SGML as an information standard to store it
- Date
- 22.11.1998 18:57:22
- Theme
- Data Mining
-
Keim, D.A.: Data Mining mit bloßem Auge (2002)
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- Abstract
- Visualisierungen, die möglichst instruktive grafische Darstellung von Daten, ist wesentlicher Bestandteil des Data Mining
- Footnote
- Teil eines Heftthemas 'Data Mining'
- Series
- Data Mining
- Theme
- Data Mining
-
Saz, J.T.: Perspectivas en recuperacion y explotacion de informacion electronica : el 'data mining' (1997)
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- Abstract
- Presents the concept and the techniques identified by the term data mining. Explains the principles and phases of developing a data mining process, and the main types of data mining tools
- Footnote
- Übers. des Titels: Perspectives on the retrieval and exploitation of electronic information: data mining
- Theme
- Data Mining
-
Wrobel, S.: Lern- und Entdeckungsverfahren (2002)
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- Abstract
- Betrügerische Kreditkartenkäufe, besonders fähige Basketballspieler und umweltbewusste Saftverkäufer ausfindig machen - Data-Mining-Verfahren lernen selbständig das Wesentliche
- Footnote
- Teil eines Heftthemas 'Data Mining'
- Series
- Data Mining
- Theme
- Data Mining
-
Peters, G.; Gaese, V.: ¬Das DocCat-System in der Textdokumentation von G+J (2003)
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- Abstract
- Wir werden einmal die Grundlagen des Text-Mining-Systems bei IBM darstellen, dann werden wir das Projekt etwas umfangreicher und deutlicher darstellen, da kennen wir uns aus. Von daher haben wir zwei Teile, einmal Heidelberg, einmal Hamburg. Noch einmal zur Technologie. Text-Mining ist eine von IBM entwickelte Technologie, die in einer besonderen Ausformung und Programmierung für uns zusammengestellt wurde. Das Projekt hieß bei uns lange Zeit DocText Miner und heißt seit einiger Zeit auf Vorschlag von IBM DocCat, das soll eine Abkürzung für Document-Categoriser sein, sie ist ja auch nett und anschaulich. Wir fangen an mit Text-Mining, das bei IBM in Heidelberg entwickelt wurde. Die verstehen darunter das automatische Indexieren als eine Instanz, also einen Teil von Text-Mining. Probleme werden dabei gezeigt, und das Text-Mining ist eben eine Methode zur Strukturierung von und der Suche in großen Dokumentenmengen, die Extraktion von Informationen und, das ist der hohe Anspruch, von impliziten Zusammenhängen. Das letztere sei dahingestellt. IBM macht das quantitativ, empirisch, approximativ und schnell. das muss man wirklich sagen. Das Ziel, und das ist ganz wichtig für unser Projekt gewesen, ist nicht, den Text zu verstehen, sondern das Ergebnis dieser Verfahren ist, was sie auf Neudeutsch a bundle of words, a bag of words nennen, also eine Menge von bedeutungstragenden Begriffen aus einem Text zu extrahieren, aufgrund von Algorithmen, also im Wesentlichen aufgrund von Rechenoperationen. Es gibt eine ganze Menge von linguistischen Vorstudien, ein wenig Linguistik ist auch dabei, aber nicht die Grundlage der ganzen Geschichte. Was sie für uns gemacht haben, ist also die Annotierung von Pressetexten für unsere Pressedatenbank. Für diejenigen, die es noch nicht kennen: Gruner + Jahr führt eine Textdokumentation, die eine Datenbank führt, seit Anfang der 70er Jahre, da sind z.Z. etwa 6,5 Millionen Dokumente darin, davon etwas über 1 Million Volltexte ab 1993. Das Prinzip war lange Zeit, dass wir die Dokumente, die in der Datenbank gespeichert waren und sind, verschlagworten und dieses Prinzip haben wir auch dann, als der Volltext eingeführt wurde, in abgespeckter Form weitergeführt. Zu diesen 6,5 Millionen Dokumenten gehören dann eben auch ungefähr 10 Millionen Faksimileseiten, weil wir die Faksimiles auch noch standardmäßig aufheben.
- Date
- 22. 4.2003 11:45:36
- Theme
- Data Mining
-
Tunbridge, N.: Semiology put to data mining (1999)
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- Theme
- Data Mining
-
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
- Theme
- Data Mining
-
Kruse, R.; Borgelt, C.: Suche im Datendschungel (2002)
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- Footnote
- Teil eines Heftthemas 'Data Mining'
- Series
- Data Mining
- Theme
- Data Mining
-
Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P.: From data mining to knowledge discovery in databases (1996)
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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
-
Schmid, J.: Data mining : wie finde ich in Datensammlungen entscheidungsrelevante Muster? (1999)
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- Theme
- Data Mining
-
Fong, A.C.M.: Mining a Web citation database for document clustering (2002)
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- Theme
- Data Mining
-
Blake, C.: Text mining (2011)
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- Theme
- Data Mining
-
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
- Theme
- Data Mining
-
Tonkin, E.L.; Tourte, G.J.L.: Working with text. tools, techniques and approaches for text mining (2016)
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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
-
Kulathuramaiyer, N.; Maurer, H.: Implications of emerging data mining (2009)
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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
-
Raghavan, V.V.; Deogun, J.S.; Sever, H.: Knowledge discovery and data mining : introduction (1998)
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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
-
Zhou, L.; Chaovalit, P.: Ontology-supported polarity mining (2008)
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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
-
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
- Theme
- Data Mining
-
Fayyad, U.M.: Data mining and knowledge dicovery : making sense out of data (1996)
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- Abstract
- Defines knowledge discovery and data mining (KDD) as the overall process of extracting high level knowledge from low level data. Outlines the KDD process. Explains how KDD is related to the fields of: statistics, pattern recognition, machine learning, artificial intelligence, databases and data warehouses
- Theme
- Data Mining
