Taeho Jo

Text Mining (eBook, PDF)

Concepts, Implementation, and Big Data Challenge

• Format: PDF

Produktbeschreibung

This book discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management.

• Presents techniques of preprocessing texts into structured forms;
• Outlines concepts of text categorization and clustering, their algorithms, and implementation guides;
• Includes advanced topics such as text summarization, text segmentation, topic mapping, and automatic text management.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 373
• Erscheinungstermin: 7. Juni 2018
• Englisch
• ISBN-13: 9783319918150
• Artikelnr.: 52893132

Autorenporträt

Dr. Taeho Jo works as a faculty member for school of game in Hongik University, South Korea. He received his PhD from University of Ottawa in 2006. His research spans text mining, neural networks, machine learning, and information retrieval. He has four years' experience working for industrial organizations and ten years' experience working for in academia. He has published almost 150 research papers, and he was awarded two times in the world wide biography dictionary, "Marquis Who's Who in the World".

Inhaltsangabe

Part I: Foundation.- Introduction.- Text Indexing.- Text Encoding.- Text Association.- Part II: Text Categorization.- Text Categorization: Conceptual View.- Text Categorization: Approaches.- Text Categorization: Implementation.- Text Categorization: Evaluation.- Part III: Text Clustering.- Text Clustering: Conceptual View.- Text Clustering: Approaches.- Text Clustering: Implementation.- Text Clustering: Evaluation.- Part IV: Advanced Topics.- Text Summarization.- Text Segmentation.- Taxonomy Generation.- Dynamic Document Organization.- References.- Index.