The proliferation of digital computing devices and their use in communication has resulted in an increased demand for systems and algorithms capable of mining textual data. Thus, the development of techniques for mining unstructured, semi-structured, and fully-structured textual data has become increasingly important in both academia and industry.

This second volume continues to survey the evolving field of text mining - the application of techniques of machine learning, in conjunction with natural language processing, information extraction and algebraic/mathematical approaches, to computational information retrieval. Numerous diverse issues are addressed, ranging from the development of new learning approaches to novel document clustering algorithms, collectively spanning several major topic areas in text mining.

Features:

a [ Acts as an important benchmark in the development of current and future approaches to mining textual information

a [ Serves as an excellent companion text for courses in text and data mining, information retrieval and computational statistics

a [ Experts from academia and industry share their experiences in solving large-scale retrieval and classification problems

a [ Presents an overview of current methods and software for text mining

a [ Highlights open research questions in document categorization and clustering, and trend detection

a [ Describes new application problems in areas such as email surveillance and anomaly detection

Survey of Text Mining II offers a broad selection in state-of-the art algorithms and software for text mining from both academic and industrial perspectives, to generate interest and insight into the stateof the field. This book will be an indispensable resource for researchers, practitioners, and professionals involved in information retrieval, computational statistics, and data mining.

Michael W. Berry is a professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville.

Malu Castellanos is a senior researcher at Hewlett-Packard Laboratories in Palo Alto, California.

This Second Edition brings readers thoroughly up to date with the emerging field of text mining, the application of techniques of machine learning in conjunction with natural language processing, information extraction, and algebraic/mathematical approaches to computational information retrieval. The book explores a broad range of issues, ranging from the development of new learning approaches to the parallelization of existing algorithms. Authors highlight open research questions in document categorization, clustering, and trend detection. In addition, the book describes new application problems in areas such as email surveillance and anomaly detection.

This book constitutes the thoroughly refereed conference proceedings of the 5th International Workshop on Business Intelligence for the Real-Time Enterprise, BIRTE 2011, held in Seattle, WA, USA, in September 2011, in conjunction with VLDB 2011, the International Conference on Very Large Data Bases. The series of BIRTE workshops aims to provide a forum for researchers to discuss and advance the foundational science and engineering required to enable real-time business intelligence as well as novel applications and solutions based on these foundational techniques.The volume contains 6 research papers, which have been carefully reviewed and selected from 12 submissions, plus the 3 keynotes presented at the workshop. The topics cover all stages of the business intelligence cycle, including capturing of real-time data, handling of temporal or uncertain data, performance issues, event management, and the optimization of complex ETL workflows. The volume contains 6 research papers, which have been carefully reviewed and selected from 12 submissions, plus the 3 keynotes presented at the workshop. The topics cover all stages of the business intelligence cycle, including capturing of real-time data, handling of temporal or uncertain data, performance issues, event management, and the optimization of complex ETL workflows.