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Textbook Of Economic Theory

History of Economic Theory
by T. Negishi

This volume aims to interest students of modern economic theory in the history of economics. For this purpose, past economic theories are considered from the point of view of current economic theories and translated, if possible and necessary, into mathematical models. It is emphasized that the currently dominating mainstream theory is not the only possible theory, and that there are many past theories which have important significance to the advancement of economic theory in the present situation, or will have it in the near future.

After a brief discussion on the history of economics from the point of view of contemporary economic theory, a bird’s-eye view of the historical development of economics is given so that readers can see the significance of topics to be discussed in subsequent chapters in a proper historical perspective. These topics are carefully chosen to show not only what great economists in the past contributed to the development of economics, but also what suggestions for solving our own current problems we can obtain by reworking problems they had to face.

The book can be used in advanced undergraduate as well as graduate classes on the history of economics. Mathematical techniques used can easily be understood by advanced undergraduates of economics major, since some models constructed originally by contemporary mathematical economists are carefully reformulated without losing the essence, basic calculus and the rudiments of linear algebra being sufficient for understanding.


A Textbook Of Economic Theory, 5/E
by Stonier

This book is for students with little or no previous knowledge of economic theory who intend to study the subject systematically and provides a general introduction to the theory while not including the special problems of international trade, public finance and welfare economics. Some parts of the book will be useful to more advanced students. The changes in this edition concentrate on the developments in macro-economic theory resulting from the interaction recently between the ‘monetarists’ and ‘Keynesians’, and one chapter now gives an account of ‘monetarism’ and the succeeding chapter presents the refinements and extension of Kaynes’ own ideas.

Economic Theory
by Gary S Becker

Others might have called this book Micro Theory or Price Theory. Becker’s choice of Economic Theory as the title for his book reflects his deep belief that there is only one kind of economic theory, not separate theories for micro problems, macro problems, non-market decisions, and so on. Indeed, as he notes, the most promising development in recent years in the literature on large scale economic problems such as unemployment has been the increasing reliance on utility maximization, a concept generally identified with microeconomics.

Microeconomics is the subject matter of this volume, but it is emphatically not confined to microeconomics in the literal sense of micro units like firms or households. Becker’s main interest is in market behavior of aggregations of firms and households. Although important inferences are drawn about individual firms and households, the author tries to understand aggregate responses to changes in basic economic parameters like tax rates, tariff schedules, technology, or antitrust provisions. His discussion is related to the market sector in industrialized economies, but the principles developed are applied to other sectors and different kinds of choices.

Becker argues that economic analysis is essential to understand much of the behavior traditionally studied by sociologists, anthropologists, and other social scientists. The broad definition of economics in terms of scarce means and competing ends is taken seriously and should be a source of pride to economists since it provides insights into a wide variety of problems. Practically all statements proved mathematically are also provided geometrically or verbally in the body of the text.


Optimization in Economic Theory
by Avinash K. Dixit, John J F Sherrerd ’52 Professor Avinash K Dixit

Building on a base of simple economic theory and elementary linear algebra and calculus, this broad treatment of static and dynamic optimization methods discusses the importance of shadow prices, and reviews functions defined by solutions of optimization problems. Recently revised and expanded, the second edition will be a valuable resource for upper level undergraduate and graduate students.

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Data Mining Handbook

Handbook of Statistical Analysis and Data Mining Applications
by Robert Nisbet, Gary Miner, Ken Yale

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application.

This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce.

  • Includes input by practitioners for practitioners
  • Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models
  • Contains practical advice from successful real-world implementations
  • Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions
  • Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Handbook of Educational Data Mining
by Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy, Ryan S.J.d. Baker

Handbook of Educational Data Mining (EDM) provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems that EDM has addressed.

Researchers at the Forefront of the Field Discuss Essential Topics and the Latest Advances
With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It brings the educational and data mining communities together, helping education experts understand what types of questions EDM can address and helping data miners understand what types of questions are important to educational design and educational decision making.

Encouraging readers to integrate EDM into their research and practice, this timely handbook offers a broad, accessible treatment of essential EDM techniques and applications. It provides an excellent first step for newcomers to the EDM community and for active researchers to keep abreast of recent developments in the field.


Magnetic Bubble Technology
by A. H. Eschenfelder

Magnetic bubbles are of interest to engineers because their properties can be used for important practical electronic devices and they are of interest to physicists because their properties are manifestations of intriguing physical principles. At the same time, the fabrication of useful configurations challenges the materials scientists and engineers. A technology of magnetic bubbles has developed to the point where commercial products are being marketed. In addition, new discovery and development are driving this technology toward substantially lower costs and presumably broader application. For all of these reasons there is a need to educate newcomers to this field in universities and in industry. The purpose of this book is to provide a text for a one-semester course that can be taught under headings of Solid State Physics, Materials Science, Computer Technology or Integrated Electronics. It is expected that the student of anyone of these disciplines will be interested in each of the chapters of this book to some degree, but may concentrate on some more than others, depending on the discipline. At the end of each chapter there is a brief summary which will serve as a reminder of the contents of the chapter but can also be read ahead of time to determine the depth of your interest in the chapter.

Data Mining and Knowledge Discovery Handbook
by Oded Maimon, Lior Rokach

Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data.

Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This handbook first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security.

Data Mining and Knowledge Discovery Handbook, 2nd Edition is designed for research scientists, libraries and advanced-level students in computer science and engineering as a reference. This handbook is also suitable for professionals in industry, for computing applications, information systems management, and strategic research management.


Handbook of Data Mining and Knowledge Discovery
by Willi Klösgen, Willi Klosgen, Jan M. Żytkow

Data mining, or knowledge discovery in databases (KDD), is one of the fastest growing areas in computing application: it offers powerful tools to analyze the many large data bases used in business, science, and industry. Data mining technology searches large databases to extract information and patterns that can be translated into useful applications, such as classifying or predicting customer behavior. This book brings together fundamental knowledge on all aspects of data mining–concepts, theory, techniques, applications, and case studies. Designed for students and professionals in such fields as computing applications, information systems management and strategic research and management, the Handbook is a comprehensive guide to essential tools and technology, from neural networks to artificial intelligence. There is a strong emphasis on real-world case studies in such areas as banking, finance, marketing management, real estate, engineering, medicine, pharmacology, and the biosciences. A much needed resource on one of the fastest growing areas of computer applications–the development and use of tools to analyze, interpret, and make use of the enormous amounts of information stored in the world’s databases.

Data Mining and Knowledge Discovery Handbook
by Oded Maimon, Lior Rokach

Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository.

This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security.

Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.


The Handbook of Data Mining
by Nong Ye

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.


The Handbook of Data Mining
by Nong Ye

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.

Data Mining and Data Visualization
by

Data Mining and Data Visualization focuses on dealing with large-scale data, a field commonly referred to as data mining. The book is divided into three sections. The first deals with an introduction to statistical aspects of data mining and machine learning and includes applications to text analysis, computer intrusion detection, and hiding of information in digital files. The second section focuses on a variety of statistical methodologies that have proven to be effective in data mining applications. These include clustering, classification, multivariate density estimation, tree-based methods, pattern recognition, outlier detection, genetic algorithms, and dimensionality reduction. The third section focuses on data visualization and covers issues of visualization of high-dimensional data, novel graphical techniques with a focus on human factors, interactive graphics, and data visualization using virtual reality. This book represents a thorough cross section of internationally renowned thinkers who are inventing methods for dealing with a new data paradigm.

  • Distinguished contributors who are international experts in aspects of data mining
  • Includes data mining approaches to non-numerical data mining including text data, Internet traffic data, and geographic data
  • Highly topical discussions reflecting current thinking on contemporary technical issues, e.g. streaming data
  • Discusses taxonomy of dataset sizes, computational complexity, and scalability usually ignored in most discussions
  • Thorough discussion of data visualization issues blending statistical, human factors, and computational insights

The Text Mining Handbook
by Ronen Feldman, James Sanger

Text mining is a new and exciting area of computer science research that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. Similarly, link detection – a rapidly evolving approach to the analysis of text that shares and builds upon many of the key elements of text mining – also provides new tools for people to better leverage their burgeoning textual data resources. The Text Mining Handbook presents a comprehensive discussion of the state-of-the-art in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection in such varied fields as M&A business intelligence, genomics research and counter-terrorism activities.