Text Mining: Classification, Clustering, and Applications

ISBN-13: 9781420059403 / Angielski / Twarda / 2009 / 328 str.

Ashok Srivastava;Mehran Sahami;Vipin Kumar

Opis

An extension of data mining, text mining involves the extraction of information and knowledge from unstructured text. This constantly evolving field is increasingly used by major corporations, such as Google, Yahoo, and Microsoft.   Featuring contributions from leading researchers in the field, this book provides a detailed overview of text mining theory, applications, and visualization. The theory section discusses text mining, information retrieval, latent semantic analysis, pagerank, latent Dirichlet allocation, and probabilistic relational models. In the section on text mining applications, the book explores web-based information, system and safety issues, spam filtering, information extraction, link mining, question answering, determining trends, and news reports. The final section of the book covers text visualization and examines how text mining techniques can be used to map information through visualizing databases.

Spis treści

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.