Text Mining: Concepts, Implementation, and Big Data Challenge: 45 (Studies in Big Data, 45) - Tapa dura

Acerca del autor

Dr. Taeho Jo The author of this book, Taeho Jo, is the founder of the publishing company, Alpha AI Publication, to which the copyright of this book belongs to, and a professor in KENTECH (Korea Institute of Energy Technology). His specialty is artificial intelligence; he got a Bachelor from Korea University, a Master from POSTECH (Pohang University of Science and Technology), and a PhD from University of Ottawa. He has careers in both industrial organizations, Samsung SDS, ETRI (Electronic and Telecommunication Research Institute), and KISTI (Korea Institute of Science and Technology Information) and academic organizations, Inha University, Hongik University, and KENTECH as a professor. He has published more than 260 research papers and 30 books with almost solo-author, awarded three times in Marquis Who’s Who in the World, and granted the noble title, Duke, from United Kingdom in 2018. The author of this book, Taeho Jo, has a very strong vision for the future as a pioneer of artificial intelligence; he recently established the international organization, UAIN (United Artificial Intelligence Nation) for propagating AI techniques globally.

De la contraportada

This popular book, updated as a textbook for classroom use, 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. The book features exercises and code to help readers quickly learn and apply knowledge.

• Presents an array of updated techniques for preprocessing texts into structured forms, geared for classroom use;
• 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.