Automatic Text Categorization and Clustering are becoming more and more important as the amount of text in electronic format grows and the access to it becomes more necessary and widespread. Well known applications are spam filtering and web search, but a large number of everyday uses exist (intelligent web search, data mining, law enforcement, etc.) Currently, researchers are employing many intelligent techniques for text categorization and clustering, ranging from support vector machines and neural networks to Bayesian inference and algebraic methods, such as Latent Semantic Indexing.
This volume offers a wide spectrum of research work developed for intelligent text categorization and clustering. In the following, we give a brief introduction of the chapters that are included in this book.
Author(s): Helyane Bronoski Borges, Julio Cesar Nievola (auth.), Nadia Nedjah, Luiza de Macedo Mourelle, Janusz Kacprzyk, Felipe M. G. França, Alberto Ferreira de De Souza (eds.)
Series: Studies in Computational Intelligence 164
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2009
Language: English
Pages: 120
Tags: Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics); Computational Linguistics; Document Preparation and Text Processing; Language Translation and Linguistics
Front Matter....Pages -
Gene Selection from Microarray Data....Pages 1-23
Preprocessing Techniques for Online Handwriting Recognition....Pages 25-45
A Simple and Fast Term Selection Procedure for Text Clustering....Pages 47-64
Bilingual Search Engine and Tutoring System Augmented with Query Expansion....Pages 65-79
Comparing Clustering on Symbolic Data....Pages 81-94
Exploring a Genetic Algorithm for Hypertext Documents Clustering....Pages 95-117
Back Matter....Pages -
