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:

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

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

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

Presents an overview of current methods and software for text mining

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

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 state of 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.