1st Edition

Text Mining Classification, Clustering, and Applications

Edited By Ashok N. Srivastava, Mehran Sahami Copyright 2009
    328 Pages 85 B/W Illustrations
    by Chapman & Hall

    The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the Field

    Giving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications 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 chapters on the classification of documents into predefined categories. It presents state-of-the-art algorithms and their use in practice. The next chapters describe novel methods for clustering documents into groups that are not predefined. These methods seek to automatically determine topical structures that may exist in a document corpus. The book concludes by discussing various text mining applications that have significant implications for future research and industrial use.

    There is no doubt that text mining will continue to play a critical role in the development of future information systems and advances in research will be instrumental to their success. This book captures the technical depth and immense practical potential of text mining, guiding readers to a sound appreciation of this burgeoning field.

    Analysis of Text Patterns Using Kernel Methods
    Marco Turchi, Alessia Mammone, and Nello Cristianini


    General Overview on Kernel Methods

    Kernels for Text


    Conclusion and Further Reading

    Detection of Bias in Media Outlets with Statistical Learning Methods
    Blaz Fortuna, Carolina Galleguillos, and Nello Cristianini


    Overview of the Experiments

    Data Collection and Preparation

    News Outlet Identification

    Topic-Wise Comparison of Term Bias

    News Outlets Map

    Related Work


    Appendix A: Support Vector Machines

    Appendix B: Bag of Words and Vector Space Models

    Appendix C: Kernel Canonical Correlation Analysis

    Appendix D: Multidimensional Scaling

    Collective Classification for Text Classification
    Galileo Namata, Prithviraj Sen, Mustafa Bilgic, and Lise Getoor


    Collective Classification: Notation and Problem Definition

    Approximate Inference Algorithms for Approaches Based on Local Conditional Classifiers

    Approximate Inference Algorithms for Approaches Based on Global Formulations

    Learning the Classifiers

    Experimental Comparison

    Related Work


    Topic Models
    David M. Blei and John D. Lafferty


    Latent Dirichlet Allocation (LDA)

    Posterior Inference for LDA

    Dynamic Topic Models and Correlated Topic Models


    Nonnegative Matrix and Tensor Factorization for Discussion Tracking
    Brett W. Bader, Michael W. Berry, and Amy N. Langville



    Tensor Decompositions and Algorithms

    Enron Subset

    Observations and Results

    Visualizing Results of the NMF Clustering

    Future Work

    Text Clustering with Mixture of von Mises–Fisher Distributions
    Arindam Banerjee, Inderjit Dhillon, Joydeep Ghosh, and Suvrit Sra


    Related Work


    EM on a Mixture of vMFs (moVMF)

    Handling High-Dimensional Text Datasets


    Experimental Results


    Conclusions and Future Work

    Constrained Partitional Clustering of Text Data: An Overview
    Sugato Basu and Ian Davidson


    Uses of Constraints

    Text Clustering

    Partitional Clustering with Constraints

    Learning Distance Function with Constraints

    Satisfying Constraints and Learning Distance Functions



    Adaptive Information Filtering
    Yi Zhang


    Standard Evaluation Measures

    Standard Retrieval Models and Filtering Approaches

    Collaborative Adaptive Filtering

    Novelty and Redundancy Detection

    Other Adaptive Filtering Topics

    Utility-Based Information Distillation
    Yiming Yang and Abhimanyu Lad


    A Sample Task

    Technical Cores

    Evaluation Methodology


    Experiments and Results

    Concluding Remarks

    Text Search Enhanced with Types and Entities
    Soumen Chakrabarti, Sujatha Das, Vijay Krishnan, and Kriti Puniyani

    Entity-Aware Search Architecture

    Understanding the Question

    Scoring Potential Answer Snippets

    Indexing and Query Processing




    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.

    … a very good overview of some state-of-the-art capabilities. … In summary, the book provides several algorithms for text mining classification, clustering, and applications, including both mathematical background and experimental observations. For readers interested in specific areas, there are several useful references. Researchers can use this book to learn more about today's field of text mining.
    Computing Reviews, March 2010

    … Not long ago people were expressing concern about the deluge of information with which we were being faced. Tools such as those described in this book present one way in which we might cope with this deluge. The separate contributions are well written, and there does seem to be a consistency which can only have arisen from sound editorial work … . This would be a perfect volume to give a new Ph.D. student about to start work on statistical and data mining methods of text analysis, and perhaps casting about for a particular area of methodology on which to focus, or for a particular application area to address. It provides a first-class overview of the scope of an area which can only grow in importance in the coming years.
    —David J. Hand, International Statistical Review, 2010

    This book is a worthy contribution to the field of text mining. By focusing on classification (rather than exhaustively covering extraction, summarization, and other tasks), it achieves the right balance of coherence and comprehensiveness. It collects papers by the leading authors in the field, who employ and explain a variety of techniques—kernel methods, link analysis, latent Dirichlet allocation, non-negative matrix factorization, and others. Together the papers bring unity and clarity to a disjointed and sometimes perplexing field and serve as the perfect introduction for an advanced student.
    —Peter Norvig, Director of Research, Google, Inc., Mountain View, California, USA

    This is a state-of-the-art, outstanding collection of overviews on text mining by a group of leading researchers in the field. The book meets an imminent need for an up-to-date overview of this exciting, dynamic research frontier and may serve as an excellent textbook on text mining for graduate students and researchers in the field as well.
    —Jiawei Han, University of Illinois at Urbana-Champaign, USA