Survey of Text Mining II: Clustering, Classification, and Retrieval

Peg Howland, Haesun Park (auth.), Michael W. Berry, Malu Castellanos (eds.)1848000456, 978-1-84800-045-2, 978-1-84800-046-9

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:

• 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.

Table of contents :
Front Matter….Pages i-xv
Cluster-Preserving Dimension Reduction Methods for Document Classification….Pages 3-23
Automatic Discovery of SimilarWords….Pages 25-44
Principal Direction Divisive Partitioning with Kernels and k -Means Steering….Pages 45-64
Hybrid Clustering with Divergences….Pages 65-85
Text Clustering with Local Semantic Kernels….Pages 87-105
Vector Space Models for Search and Cluster Mining….Pages 109-127
Applications of Semidefinite Programming in XML Document Classification….Pages 129-144
Discussion Tracking in Enron Email Using PARAFAC….Pages 147-163
Spam Filtering Based on Latent Semantic Indexing….Pages 165-183
A Probabilistic Model for Fast and Confident Categorization of Textual Documents….Pages 187-202
Anomaly Detection Using Nonnegative Matrix Factorization….Pages 203-217
Document Representation and Quality of Text: An Analysis….Pages 219-232
Back Matter….Pages 233-240