Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 16. Dezember 2019)
1Smiraglia, R.P. u. H.-L. Lee (Hrsg.): Cultural frames of knowledge.
Würzburg : Ergon Verlag, 2012. X, 158 S.
Inhalt: Ch. 1. Introduction: theory, knowledge organization, epistemology, culture -- ch. 3. Praxes of knowledge organization in the first Chinese library catalog, the Seven epitomes -- ch. 4. Feminist epistemologies and knowledge organization -- ch. 5. Problems and characteristics of Foucauldian discourse analysis as a research method -- ch. 6. Epistemology of domain analysis -- ch. 8. Rethinking genre in knowledge organization through a functional unit taxonomy -- Conclusions: Toward multicultural domain plurality in knowledge organization
Anmerkung: Rez. in: KO 42(2915) no.2, S.129-133 (R. Szostak)
LCSH: Information organization ; Social epistemology ; Knowledge management
RSWK: Wissensorganisation / Wissensmanagement / Informationsgesellschaft / Aufsatzsammlung
DDC: 020 / DDC22ger
GHBS: PZY (FH K)
RVK: AN 93000 ; MS 6950 ; ST 515
2Kantardzic, M.: Data mining : concepts, models, methods, and algorithms.
Hoboken, NJ : Wiley-Interscience, 2003. XII, 345 S.
Abstract: This book offers a comprehensive introduction to the exploding field of data mining. We are surrounded by data, numerical and otherwise, which must be analyzed and processed to convert it into information that informs, instructs, answers, or otherwise aids understanding and decision-making. Due to the ever-increasing complexity and size of today's data sets, a new term, data mining, was created to describe the indirect, automatic data analysis techniques that utilize more complex and sophisticated tools than those which analysts used in the past to do mere data analysis. "Data Mining: Concepts, Models, Methods, and Algorithms" discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples. This text offers guidance: how and when to use a particular software tool (with their companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. This book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. Data mining is an exploding field and this book offers much-needed guidance to selecting among the numerous analysis programs that are available.
Themenfeld: Data Mining
LCSH: Data mining
RSWK: Data Mining / Lehrbuch
BK: 06.74 Informationssysteme
DDC: 006.3/12 / dc22
GHBS: TWX (E) ; PZY (FH K)
LCC: QA76.9.D343K36 2003
RVK: ST 270 ; ST 530