Giving a broad perspective of the field from numerous vantage points, Text Mining focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas, including adaptive information filtering, information distillation, and text search. The book begins with the classification of documents into predefined categories and then describes novel methods for clustering documents into groups that are not predefined. It concludes with various text mining applications that have significant implications for future research and industrial use.

Ashok N. Srivastava is the Principal Investigator of the Integrated Vehicle Health Management research project in the NASA Aeronautics Research Mission Directorate. Dr. Srivastava also leads the Intelligent Data Understanding group at NASA Ames Research Center.

Mehran Sahami is an Associate Professor and Associate Chair for Education in the computer science department at Stanford University.

Analysis of Text Patterns Using Kernel Methods. Detection of Bias in Media Outlets with Statistical Learning Methods. Collective Classification for Text Classification. Topic Models. Nonnegative Matrix and Tensor Factorization for Discussion Tracking. Text Clustering with Mixture of von Mises-Fisher Distributions. Constrained Partitional Clustering of Text Data: An Overview. Adaptive Information Filtering. Utility-Based Information Distillation. Text Search Enhanced with Types and Entities. Index.