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.

Universal UX Design: Building Multicultural User Experience provides an ideal guide as multicultural UX continues to emerge as a transdisciplinary field that, in addition to the traditional UI and corporate strategy concerns, includes socio/cultural and neurocognitive concerns that constitute one of the first steps in a truly global product strategy. In short, multicultural UX is no longer a nice-to-have in your overall UX strategy, it is now a must-have. This practical guide teaches readers about international concerns on the development of a uniquely branded, yet culturally appealing, software end-product. With hands-on examples throughout, readers will learn how to accurately predict user behavior, optimize layout and text elements, and integrate persuasive design in layout, as well as how to determine which strategies to communicate image and content more effectively, while demystifying the psychological and sociopolitical factors associated with culture. The book reviews the essentials of cognitive UI perception and how they are affected by socio-cultural conditioning, as well as how different cultural bias and expectations can work in UX design.

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.