Del i serien Studies in Big Data

Text Mining

Concepts, Implementation, and Big Data Challenge

Häftad, Engelska, 2019

AvTaeho Jo

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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.

Produktinformation

Utgivningsdatum2019-02-14
Mått155 x 235 x undefined mm
Vikt587 g
FormatHäftad
SpråkEngelska
SerieStudies in Big Data
Antal sidor373
FörlagSpringer Nature Switzerland AG
ISBN9783030063023

Tillhör följande kategorier

Systemvetenskap och AI inom Data och IT
Elektronik och kommunikationer inom Naturvetenskap och teknik
Artificiell intelligens inom Data och IT
Databaser inom Data och IT

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”.

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.