Data Mining Methods for the Content Analyst: An Introduction to the Computational Analysis of Content

Leetaru, Kalev (Author)

ISBN 10: 0415895138 / ISBN 13: 9780415895132

Verlag: Routledge, 2011

Beschreibung

1st edition. 184 pages. 9.06x6.22x0.47 inches. In Stock.

Bestandsnummer des Verkäufers x-0415895138

Bibliografische Details

• Titel: Data Mining Methods for the Content Analyst: An Introduction to the Computational Analysis of Content
• Verlag: Routledge
• Veröffentlichungsdatum: 2011
• Sprache: Englisch
• ISBN-10: 0415895138
• ISBN-13: 9780415895132
• Bindung: Hardcover
• Zustand: Brand New

Über diesen Titel

Inhaltsangabe

With continuous advancements and an increase in user popularity, data mining technologies serve as an invaluable resource for researchers across a wide range of disciplines in the humanities and social sciences. In this comprehensive guide, author and research scientist Kalev Leetaru introduces the approaches, strategies, and methodologies of current data mining techniques, offering insights for new and experienced users alike.

Designed as an instructive reference to computer-based analysis approaches, each chapter of this resource explains a set of core concepts and analytical data mining strategies, along with detailed examples and steps relating to current data mining practices. Every technique is considered with regard to context, theory of operation and methodological concerns, and focuses on the capabilities and strengths relating to these technologies. In addressing critical methodologies and approaches to automated analytical techniques, this work provides an essential overview to a broad innovative field.

Über die Autorin bzw. den Autor

Kalev Leetaru is Senior Research Scientist for Content Analysis at the University of Illinois Institute for Computing in Humanities, Arts, and Social Science and Center Affiliate of the National Center for Supercomputing Applications. He leads a number of large initiatives centering on the application of high performance computing to grand challenge problems using massive-scale document and data archives.