Survey of Text Mining Clustering, Classification, and Retrieval

Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory.

As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments.

This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.

I Clustering and Classification.- 1 Cluster-Preserving Dimension Reduction Methods for Efficient Classification of Text Data.- 2 Automatic Discovery of Similar Words.- 3 Simultaneous Clustering and Dynamic Keyword Weighting for Text Documents.- 4 Feature Selection and Document Clustering.- II Information Extraction and Retrieval.- 5 Vector Space Models for Search and Cluster Mining.- 6 HotMiner: Discovering Hot Topics from Dirty Text.- 7 Combining Families of Information Retrieval Algorithms Using Metalearning.- III Trend Detection.- 8 Trend and Behavior Detection from Web Queries.- 9 A Survey of Emerging Trend Detection in Textual Data Mining.