Intelligent Text Categorization and Clustering [electronic resource] / edited by Nadia Nedjah, Luiza Macedo Mourelle, Janusz Kacprzyk, Felipe M. G. França, Alberto Ferreira De Souza.Material type: TextLanguage: English Series: Studies in Computational Intelligence: 164Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009Description: online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540856443Subject(s): Engineering | Artificial intelligence | Text processing (Computer science | Translators (Computer programs) | Computational linguistics | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics) | Computational Linguistics | Document Preparation and Text Processing | Language Translation and LinguisticsAdditional physical formats: Printed edition:: No titleDDC classification: 519 LOC classification: TA329-348TA640-643Online resources: Click here to access online
Gene Selection from Microarray Data -- Preprocessing Techniques for Online Handwriting Recognition -- A Simple and Fast Term Selection Procedure for Text Clustering -- Bilingual Search Engine and Tutoring System Augmented with Query Expansion -- Comparing Clustering on Symbolic Data -- Exploring a Genetic Algorithm for Hypertext Documents Clustering.
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.