Zhengxin Chen – författare

Intelligent decision support relies on techniques from a variety of disciplines, including artificial intelligence and database management systems. Most of the existing literature neglects the relationship between these disciplines. By integrating AI and DBMS, Computational Intelligence for Decision Support produces what other texts don't: an explanation of how to use AI and DBMS together to achieve high-level decision making.Threading relevant disciplines from both science and industry, the author approaches computational intelligence as the science developed for decision support. The use of computational intelligence for reasoning and DBMS for retrieval brings about a more active role for computational intelligence in decision support, and merges computational intelligence and DBMS. The introductory chapter on technical aspects makes the material accessible, with or without a decision support background. The examples illustrate the large number of applications and an annotated bibliography allows you to easily delve into subjects of greater interest.The integrated perspective creates a book that is, all at once, technical, comprehensible, and usable. Now, more than ever, it is important for science and business workers to creatively combine their knowledge to generate effective, fruitful decision support. Computational Intelligence for Decision Support makes this task manageable.

An expert guide for applying data mining with uncertain reasoning to a wide range of uses This volume presents a holistic view of data mining by integrating this diverse and exciting field with uncertain reasoning. It treats a wide range of issues and examines the state of the art in both fields while summarizing vital concepts that can normally only be found in various separate resources. The author concentrates on practical aspects of data mining-such as infrastructure and overall processes-but also discusses some selected algorithms and performance-related issues. Several important topics are addressed specifically, such as bridging the fields of machine learning and data mining and the discovery of influential association rules. In addition, the author discusses data warehousing as an enabling technique for data mining. Case studies are included throughout to illustrate important concepts. Data Mining and Uncertain Reasoning is a practical reference for practitioners in various interrelated fields. Each subject is treated with both basic introductory and advanced technical descriptions, making the book suitable for students and practitioners at various levels of experience.

Effective decision support systems (DSS) are quickly becoming key to businesses gaining a competitive advantage, and the effectiveness of these systems depends on the ability to construct, maintain, and extract information from data warehouses. While many still perceive data warehousing as a subdiscipline of management information systems (MIS), in fact many of its advances have and will continue to come from the computer science arena.Intelligent Data Warehousing presents the state of the art in data warehousing research and practice from a perspective that integrates business applications and computer science. It brings the intelligent techniques associated with artificial intelligence (AI) to the entire process of data warehousing, including data preparation, storage, and mining. Part I provides an overview of the main ideas and fundamentals of data mining, artificial intelligence, business intelligence, and data warehousing. Part II presents core materials on data warehousing, and Part III explores data analysis and knowledge discovery in the data warehousing environment, including how to perform intelligent data analysis and the discovery of influential association patterns.Bridging the gap between theoretical research and business applications, this book summarizes the main ideas behind recent research developments rather than setting forth technical details, and it presents case studies that show the how-to's of implementing these ideas. The result is a practical, first-of-its-kind book that brings together scattered research, unites MIS with computer science, and melds intelligent techniques with data warehousing.

Intelligent decision support relies on techniques from a variety of disciplines, including artificial intelligence and database management systems. Most of the existing literature neglects the relationship between these disciplines. By integrating AI and DBMS, Computational Intelligence for Decision Support produces what other texts don't: an explanation of how to use AI and DBMS together to achieve high-level decision making.Threading relevant disciplines from both science and industry, the author approaches computational intelligence as the science developed for decision support. The use of computational intelligence for reasoning and DBMS for retrieval brings about a more active role for computational intelligence in decision support, and merges computational intelligence and DBMS. The introductory chapter on technical aspects makes the material accessible, with or without a decision support background. The examples illustrate the large number of applications and an annotated bibliography allows you to easily delve into subjects of greater interest.The integrated perspective creates a book that is, all at once, technical, comprehensible, and usable. Now, more than ever, it is important for science and business workers to creatively combine their knowledge to generate effective, fruitful decision support. Computational Intelligence for Decision Support makes this task manageable.

This book constitutes the thoroughly refereed post-proceedings of the Chinese Academy of Sciences Symposium on Data Mining and Knowledge Discovery CASDMKD 2004, held in Beijing, China in July 2004.The 25 revised full papers presented together with 3 invited keynote papers were carefully reviewed and selected from 60 submissions. The papers are organized in topical sections on data mining methodology, practical issues, data mining for bioinformatics, data mining applications, enterprise knowledge management, risk management, and integration of data mining and knowledge management.

Intelligent agents are one of the most promising business tools in our information rich world. An intelligent agent consists of a software system capable of performing intelligent tasks within a dynamic and unpredictable environment. They can be characterised by various attributes including: autonomous, adaptive, collaborative, communicative, mobile, and reactive. Many problems are not well defined and the information needed to make decisions is not available. These problems are not easy to solve using conventional computing approaches. Here, the intelligent agent paradigm may play a major role in helping to solve these problems. This book, written for application researchers, covers a broad selection of research results that demonstrate, in an authoritative and clear manner, the applications of agents within our information society.