TY  - BOOK
AU  - Jo,Taeho
ED  - SpringerLink (Online service)
TI  - Text Mining: Concepts, Implementation, and Big Data Challenge 
T2  - Studies in Big Data,
SN  - 9783031759765
AV  - Q342 
U1  - 006.3 23
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Computational intelligence
KW  - Telecommunication
KW  - Data mining
KW  - Information storage and retrieval systems
KW  - Quantitative research
KW  - Computational Intelligence
KW  - Communications Engineering, Networks
KW  - Data Mining and Knowledge Discovery
KW  - Information Storage and Retrieval
KW  - Data Analysis and Big Data
N1  - 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-75976-5
ER  -