Text Mining: Classification, Clustering, and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series v. 10)
By: Mehran Sahami (editor), Ashok N. Srivastava (editor), Vipin Kumar (series_editor)Hardback
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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.
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 Marco Turchi, Alessia Mammone, and Nello Cristianini Introduction General Overview on Kernel Methods Kernels for Text Example Conclusion and Further Reading Detection of Bias in Media Outlets with Statistical Learning Methods Blaz Fortuna, Carolina Galleguillos, and Nello Cristianini Introduction Overview of the Experiments Data Collection and Preparation News Outlet Identification Topic-Wise Comparison of Term Bias News Outlets Map Related Work Conclusion 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 Introduction 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 Conclusion Topic Models David M. Blei and John D. Lafferty Introduction Latent Dirichlet Allocation (LDA) Posterior Inference for LDA Dynamic Topic Models and Correlated Topic Models Discussion Nonnegative Matrix and Tensor Factorization for Discussion Tracking Brett W. Bader, Michael W. Berry, and Amy N. Langville Introduction Notation 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 Introduction Related Work Preliminaries EM on a Mixture of vMFs (moVMF) Handling High-Dimensional Text Datasets Algorithms Experimental Results Discussion Conclusions and Future Work Constrained Partitional Clustering of Text Data: An Overview Sugato Basu and Ian Davidson Introduction Uses of Constraints Text Clustering Partitional Clustering with Constraints Learning Distance Function with Constraints Satisfying Constraints and Learning Distance Functions Experiments Conclusions Adaptive Information Filtering Yi Zhang Introduction 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 Introduction A Sample Task Technical Cores Evaluation Methodology Data 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 Conclusion Index
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