Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, "The Handbook of Data Mining" presents comprehensive coverage of data mining concepts and techniques. Algorithms, methodologies, management issues, and tools are all illustrated through engaging examples and real-world applications to ease understanding of the materials.
This book is organized into three parts. Part I presents various data mining methodologies, concepts, and available software tools for each methodology. Part II addresses various issues typically faced in the management of data mining projects and tips on how to maximize outcome utility. Part III features numerous real-world applications of these techniques in a variety of areas, including human performance, geospatial, bioinformatics, on- and off-line customer transaction activity, security-related computer audits, network traffic, text and image, and manufacturing quality.
This "Handbook" is ideal for researchers and developers who want to use data mining techniques to derive scientific inferences where extensive data is available in scattered reports and publications. It is also an excellent resource for graduate-level courses on data mining and decision and expert systems methodology.

Many professionals and students in engineering, science, business, and other application fields need to develop Windows-based and web-enabled information systems to store and use data for decision support, without help from professional programmers. However, few books are available to train professionals and students who are not professional programmers to develop these information systems. Developing Windows-Based and Web-Enabled Information Systems fills this gap, providing a self-contained, easy-to-understand, and well-illustrated text that explores current concepts, methods, and software tools for developing Windows-based and web-enabled information systems. Written in an easily accessible style, the book details current concepts, methods, and software tools for Windows-based and web-enabled information systems that store and use data. It is self-contained with easy-to-understand small examples to walk through concepts and implementation details along with large-scale case studies. The book describes data modeling methods including entity-relationship modeling, relational modeling and normalization, and object-oriented data modeling, to develop data models of a database. The author covers how to use software tools in the Microsoft application development environment, including Microsoft Access, MySQL, SQL, Visual Studio, Visual Basic, VBA, HTML, and XML, to implement databases and develop Windows-based and web-enabled applications with the database, graphical user interface, and program components. The book takes you through the entire process of developing a computer and network application for an information system, highlighting concepts and operation details. In each chapter, small data examples are used to manually walk through concepts and operational details. These features and more give you the conceptual understanding and practical skill required, even if you don't have a computer science background, to develop Windows-based or web-enabled applications for your specialized information system.

Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, "The Handbook of Data Mining" presents comprehensive coverage of data mining concepts and techniques. Algorithms, methodologies, management issues, and tools are all illustrated through engaging examples and real-world applications to ease understanding of the materials.
This book is organized into three parts. Part I presents various data mining methodologies, concepts, and available software tools for each methodology. Part II addresses various issues typically faced in the management of data mining projects and tips on how to maximize outcome utility. Part III features numerous real-world applications of these techniques in a variety of areas, including human performance, geospatial, bioinformatics, on- and off-line customer transaction activity, security-related computer audits, network traffic, text and image, and manufacturing quality.
This "Handbook" is ideal for researchers and developers who want to use data mining techniques to derive scientific inferences where extensive data is available in scattered reports and publications. It is also an excellent resource for graduate-level courses on data mining and decision and expert systems methodology.

New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms. The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures. The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.

New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms. The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures. The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.

This book is a compilation of a selected subset of research articles presented at the Eighth INFORMS Computing Society Conference, held in Chandler, Arizona, from January 8 to 10, 2003. The articles in this book represent the diversity and depth of the interface between ORiMS (operations research and the management sciences) and CS/AI (computer science and artificial intelligence ). This volume starts with two papers that represent the reflective and integrative thinking that is critical to any scientific discipline. These two articles present philosophical perspectives on computation, covering a variety of traditional and newer methods for modeling, solving, and explaining mathematical models. The next set includes articles that study machine learning and computational heuristics, and is followed by articles that address issues in performance testing of solution algorithms and heuristics. These two sets of papers demonstrate the richness of thought that takes place at the ORiMS and CSI AI interface. The final set of articles demonstrates the usefulness of these and other methods at the interface towards solving problems in the real world, covering e-commerce, workflow, electronic negotiation, music, parallel computation, and telecommunications. The articles in this collection represent the results of cross-fertilization between ORiMS and CSI AI, making possible advances that could have not been achieved in isolation. The continuing aim ofthe INFORMS Computing Society and this research conference is to invigorate and further develop this interface.

This book is a compilation of a selected subset of research articles presented at the Eighth INFORMS Computing Society Conference, held in Chandler, Arizona, from January 8 to 10, 2003. The articles in this book represent the diversity and depth of the interface between ORiMS (operations research and the management sciences) and CS/AI (computer science and artificial intelligence ). This volume starts with two papers that represent the reflective and integrative thinking that is critical to any scientific discipline. These two articles present philosophical perspectives on computation, covering a variety of traditional and newer methods for modeling, solving, and explaining mathematical models. The next set includes articles that study machine learning and computational heuristics, and is followed by articles that address issues in performance testing of solution algorithms and heuristics. These two sets of papers demonstrate the richness of thought that takes place at the ORiMS and CSI AI interface. The final set of articles demonstrates the usefulness of these and other methods at the interface towards solving problems in the real world, covering e-commerce, workflow, electronic negotiation, music, parallel computation, and telecommunications. The articles in this collection represent the results of cross-fertilization between ORiMS and CSI AI, making possible advances that could have not been achieved in isolation. The continuing aim ofthe INFORMS Computing Society and this research conference is to invigorate and further develop this interface.

Many professionals and students in engineering, science, business, and other application fields need to develop Windows-based and web-enabled information systems to store and use data for decision support, without help from professional programmers. However, few books are available to train professionals and students who are not professional programmers to develop these information systems. Developing Windows-Based and Web-Enabled Information Systems fills this gap, providing a self-contained, easy-to-understand, and well-illustrated text that explores current concepts, methods, and software tools for developing Windows-based and web-enabled information systems. Written in an easily accessible style, the book details current concepts, methods, and software tools for Windows-based and web-enabled information systems that store and use data. It is self-contained with easy-to-understand small examples to walk through concepts and implementation details along with large-scale case studies. The book describes data modeling methods including entity-relationship modeling, relational modeling and normalization, and object-oriented data modeling, to develop data models of a database. The author covers how to use software tools in the Microsoft application development environment, including Microsoft Access, MySQL, SQL, Visual Studio, Visual Basic, VBA, HTML, and XML, to implement databases and develop Windows-based and web-enabled applications with the database, graphical user interface, and program components. The book takes you through the entire process of developing a computer and network application for an information system, highlighting concepts and operation details. In each chapter, small data examples are used to manually walk through concepts and operational details. These features and more give you the conceptual understanding and practical skill required, even if you don't have a computer science background, to develop Windows-based or web-enabled applications for your specialized information system.