Fuzzy Modeling And Genetic Algorithms For Data Mining And Exploration
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| Author | : Earl Cox |
| Publisher | : Academic Press |
| Total Pages | : 554 |
| Release | : 2005-02 |
| Genre | : Computers |
| ISBN | : 0121942759 |
Foundations and ideas -- Principal model types -- Approaches to model building -- Fundamental concepts of fuzzy logic -- Fundamental concepts of fuzzy systems -- Fuzzy SQL and intelligent queries -- Fuzzy clustering -- Fuzzy rule induction -- Fundamental concepts of genetic algorithms -- Genetic resource scheduling optimization -- Genetic tuning of fuzzy models.
| Author | : Mehmed Kantardzic |
| Publisher | : John Wiley & Sons |
| Total Pages | : 676 |
| Release | : 2019-10-23 |
| Genre | : Computers |
| ISBN | : 1119516072 |
Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author—a noted expert on the topic—explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: • Explores big data and cloud computing • Examines deep learning • Includes information on convolutional neural networks (CNN) • Offers reinforcement learning • Contains semi-supervised learning and S3VM • Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.
| Author | : Earl Cox |
| Publisher | : Elsevier |
| Total Pages | : 553 |
| Release | : 2005-02-24 |
| Genre | : Computers |
| ISBN | : 0080470599 |
Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you'll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems. You don't need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system. - Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems - Helps you to understand the trade-offs implicit in various models and model architectures - Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction - Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model - In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem - Presents examples in C, C++, Java, and easy-to-understand pseudo-code - Extensive online component, including sample code and a complete data mining workbench
| Author | : Soumen Chakrabarti |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 477 |
| Release | : 2008-10-31 |
| Genre | : Computers |
| ISBN | : 0080877885 |
This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources. - Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints. - Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader's technical expertise and ability to implement practical solutions. - Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.
| Author | : Hsinchun Chen |
| Publisher | : Springer |
| Total Pages | : 182 |
| Release | : 2010-06-03 |
| Genre | : Computers |
| ISBN | : 364213601X |
This book constitutes the proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics 2010, held in Hyderabad, India, in June 2010.
| Author | : Kurosh Madani |
| Publisher | : Springer |
| Total Pages | : 362 |
| Release | : 2012-12-22 |
| Genre | : Technology & Engineering |
| ISBN | : 3642356389 |
The present book includes a set of selected extended papers from the third International Joint Conference on Computational Intelligence (IJCCI 2011), held in Paris, France, from 24 to 26 October 2011. The conference was composed of three co-located conferences: The International Conference on Fuzzy Computation (ICFC), the International Conference on Evolutionary Computation (ICEC), and the International Conference on Neural Computation (ICNC). Recent progresses in scientific developments and applications in these three areas are reported in this book. IJCCI received 283 submissions, from 59 countries, in all continents. This book includes the revised and extended versions of a strict selection of the best papers presented at the conference.
| Author | : Vassil Sgurev |
| Publisher | : Springer |
| Total Pages | : 357 |
| Release | : 2016-02-05 |
| Genre | : Technology & Engineering |
| ISBN | : 3319272675 |
This book presents a broad variety of different contemporary IT methods and applications in Intelligent Systems is displayed. Every book chapter represents a detailed, specific, far reaching and original re-search in a respective scientific and practical field. However, all of the chapters share the common point of strong similarity in a sense of being innovative, applicable and mutually compatible with each other. In other words, the methods from the different chapters can be viewed as bricks for building the next generation “thinking machines” as well as for other futuristic logical applications that are rapidly changing our world nowadays.
| Author | : Sumeet Dua |
| Publisher | : CRC Press |
| Total Pages | : 434 |
| Release | : 2016-04-19 |
| Genre | : Computers |
| ISBN | : 1439839395 |
This comprehensive volume demonstrates the broad scope of uses for data mining and includes detailed strategies and methodologies for analyzing data from biomedical images, signals, and systems. Written by experts in the field, it presents data mining techniques in the context of various important clinical issues, including diagnosis and grading of depression, identification and classification of arrhythmia and ischemia, and description of classification paradigms for mammograms. The book provides ample information and techniques to benefit researchers, practitioners, and educators of biomedical science and engineering.
| Author | : M. Nikravesh |
| Publisher | : Elsevier |
| Total Pages | : 755 |
| Release | : 2003-04-22 |
| Genre | : Science |
| ISBN | : 0080541321 |
This comprehensive book highlights soft computing and geostatistics applications in hydrocarbon exploration and production, combining practical and theoretical aspects.It spans a wide spectrum of applications in the oil industry, crossing many discipline boundaries such as geophysics, geology, petrophysics and reservoir engineering. It is complemented by several tutorial chapters on fuzzy logic, neural networks and genetic algorithms and geostatistics to introduce these concepts to the uninitiated. The application areas include prediction of reservoir properties (porosity, sand thickness, lithology, fluid), seismic processing, seismic and bio stratigraphy, time lapse seismic and core analysis.There is a good balance between introducing soft computing and geostatistics methodologies that are not routinely used in the petroleum industry and various applications areas. The book can be used by many practitioners such as processing geophysicists, seismic interpreters, geologists, reservoir engineers, petrophysicist, geostatistians, asset mangers and technology application professionals. It will also be of interest to academics to assess the importance of, and contribute to, R&D efforts in relevant areas.
| Author | : Rob Sullivan |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 644 |
| Release | : 2012-01-07 |
| Genre | : Science |
| ISBN | : 1597452904 |
Data mining provides a set of new techniques to integrate, synthesize, and analyze tdata, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization.
