The five volume set CCIS 224-228 constitutes the refereed proceedings of the International conference on Applied Informatics and Communication, ICAIC 2011, held in Xi'an, China in August 2011. The 446 revised papers presented were carefully reviewed and selected from numerous submissions. The papers cover a broad range of topics in computer science and interdisciplinary applications including control, hardware and software systems, neural computing, wireless networks, information systems, and image processing.

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge.

Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges.

* Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

`The authors have done a magnificent job explaining this important dimension in the use of Chinese. Learners will appreciate the care they have taken to ensure that the material is maximally relevant and clear. The dictionary will be an invaluable aid to both comprehension and production.' David Crystal, Honorary Professor of Linguistics, The University of Wales, UK

`Grounded in corpus research, this dictionary has a number of laudable features: with focused attention on learner usage, entries are clearly analyzed, exemplified and contextualized.' Zheng-sheng Zhang, San Diego State University, USA

500 Common Chinese Idioms is the ideal tool for all intermediate to advanced learners of Chinese. Based on large corpuses of authentic language data, it presents the 500 most commonly used Chinese idioms or chengyu, along with a variety of synonyms, antonyms and the most common structures, enabling the reader to make educated guesses about the meanings of hundreds of unfamiliar idioms.

This practical dictionary is suitable both for class use and independent study and will be of interest to students and teachers of Chinese alike.

This book presents in-depth surveys and systematic discussions on models, algorithms and applications for link mining. Link mining is an important field of data mining. Traditional data mining focuses on 'flat' data in which each data object is represented as a fixed-length attribute vector. However, many real-world data sets are much richer in structure, involving objects of multiple types that are related to each other. Hence, recently link mining has become an emerging field of data mining, which has a high impact in various important applications such as text mining, social network analysis, collaborative filtering, and bioinformatics. At present, there are no books in the market focusing on the theory and techniques as well as the related applications for link mining. On the other hand, due to the high popularity of linkage data, extensive applications ranging from governmental organizations to commercial businesses to people's daily life call for exploring the techniques of mining linkage data; people need such a reference book to systematically apply the link mining techniques to these applications to develop the related technologies. Therefore, such a book is in high demand on the market.

This second edition includes updated and substantially revised versions of chapters from the 2001 first edition, and new chapters on topics that have become prominent during the interval. Geographers working with information systems and information scientists working with geography explain some approaches and techniques for extracting knowledge from the data-rich environment that new electronic sensing and processing have created. Their topics include fundamentals of spatial data warehousing for geographic knowledge discovery, data quality issues, clustering methods in geographical data analysis, leveraging the power of spatial data mining to enhance the applicability of geographic information systems technology, multi-variate spatial clustering and geo-visualization, and periodic pattern discovery from trajectories of moving objects. Many of the high-quality monochrome illustrations embedded in the text are also reproduced in a battery of color plates. Annotation ©2009 Book News, Inc., Portland, OR (booknews.com)

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.

Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data.

Whether you are a seasoned professional or a new student of data mining, this book has much to offer you:
* A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data.
* Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning.
* Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
* Complete classroom support for instructors at www.mkp.com/datamining2e companion site.