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检索条件"丛书名=Chapman & Hall/CRC Data Mining and Knowledge Discovery Series"

共 44 条记录，以下是21-30 订阅

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Computational intelligent data analysis for sustainable development

引用

丛书名： chapman & hall/crc data mining and knowledge discovery series

2013年

作者： Ting Yu Nitesh Chawla Simeon J Simoff

ISBN: (纸本)9781439895948;9781138198692;1439895945

Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems. With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors. The book summarizes the latest contributions of the data analysis community to climate change research. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research. The book also pays special attention to several critical challenges in the science of climate extremes that are not handled by the current generation of climate models. It discusses potential conceptual and methodological directions to build a close integration between physical understanding, or physics-based modeling, and data-driven insights. The book then covers the conservation of species and ecologically valuable land. A case study on the Pennsylvania Dirt and Gravel Roads Program demonstrates t

关键词： Public works Social policy Environmental quality data mining Sustainable development

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Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions 1

引用

丛书名： chapman & hall/crc data mining and knowledge discovery series

2012年

作者： Deng, Naiyang Tian, Yingjie Zhang, Chunhua

ISBN: (数字)9781439857939

ISBN: (纸本)9781439857922

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which

关键词： Computer Science

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data mining with R: Learning with Case Studies (chapman &hall crc data mining and knowledge discovery series) 1

引用

丛书名： chapman & hall crc data mining and knowledge discovery series

2010年

作者： Luis Torgo

ISBN: (纸本)1439810184;9781439810187

The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, data mining with R: Learning with Case Studies uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main data mining processes and techniques, the author takes a hands-on approach that utilizes a series of detailed, real-world case studies: Predicting algae blooms Predicting stock market returns Detecting fraudulent transactions Classifying microarray samples With these case studies, the author supplies all necessary steps, code, and data. Web ResourceA supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions.

关键词： data hall mini studies mining learning case chapman

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Service-Oriented Distributed knowledge discovery 1

引用

丛书名： chapman & hall/crc data mining and knowledge discovery series, Volume 27

2012年

作者： Talia Domenico Trunfio Paolo

ISBN: (数字)9780429109911

ISBN: (纸本)9781439875315

A new approach to distributed large-scale data mining, service-oriented knowledge discovery extracts useful knowledge from today's often unmanageable volumes of data by exploiting data mining and machine learning distributed models and techniques in service-oriented infrastructures. Service-Oriented Distributed knowledge discovery presents techniqu

关键词： Computer Science Engineering & Technology

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Advances in Machine Learning and data mining for Astronomy 1

引用

丛书名： chapman & hall/crc data mining and knowledge discovery series.

2012年

作者： Edited by Ashok N . Srivastava

ISBN: (数字)9781439841747

ISBN: (纸本)9781138199309

Advances in Machine Learning and data mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines

关键词： astrophysics particle databases

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Machine learning and knowledge discovery for engineering systems health management

引用

丛书名： chapman & hall/crc data mining and knowledge discovery series

2011年

作者： Ashok N Srivastava Jiawei Han

ISBN: (纸本)9781439841785;1439841780

Machine Learning and knowledge discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. With contributions from many top authorities on the subject, this volume is the first to bring together the two areas of machine learning and systems health management. Divided into three parts, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management. The first part of the text describes data-driven methods for anomaly detection, diagnosis, and prognosis of massive data streams and associated performance metrics. It also illustrates the analysis of text reports using novel machine learning approaches that help detect and discriminate between failure modes. The second part focuses on physics-based methods for diagnostics and prognostics, exploring how these methods adapt to observed data. It covers physics-based, data-driven, and hybrid approaches to studying damage propagation and prognostics in composite materials and solid rocket motors. The third part discusses the use of machine learning and physics-based approaches in distributed data centers, aircraft engines, and embedded real-time software systems. Reflecting the interdisciplinary nature of the field, this book shows how various machine learning and knowledge discovery techniques are used in the analysis of complex engineering systems. It emphasizes the importance of these techniques in managing the intricate interactions within and between the systems to maintain a high degree of reliability.

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Spectral Feature Selection For data mining 1

引用

丛书名： chapman & hall/crc data mining and knowledge discovery series

2011年

作者： Zheng Zhao and Huan Liu

ISBN: (数字)9781439862094

ISBN: (纸本)9781439862094

Spectral feature selection is a state-of-the-art technique based on spectral graph theory. This book provides a comprehensive introduction to spectra, including its theoretical foundations, connections to existing feature selection and extraction algorithms, and its applications in solving novel real-world problems in feature selection. It covers general feature selection and feature extraction concepts, the most popular existing algorithms, and recent research developments. The text also includes precise definitions, a number of illustrative figures, and plenty of examples, along with slides, source code, and data on a supporting website.

关键词： Computer Science databases data Preparation \u0026 mining Reference

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Music data mining 1

引用

丛书名： chapman and hall/crc data mining and knowledge discovery Ser.

2011年

作者： Li, Tao Ogihara, Mitsunori Taylor & Francis Group

ISBN: (数字)9781439835555

ISBN: (纸本)9781439835524

The research area of music information retrieval has gradually evolved to address the challenges of effectively accessing and interacting large collections of music and associated data, such as styles, artists, lyrics, and reviews. Bringing together an interdisciplinary array of top researchers, Music data mining presents a variety of approaches to

关键词： Engineering & Technology Mathematics & Statistics

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data Clustering in C++ 1

引用

丛书名： chapman and hall/crc data mining and knowledge discovery Ser.

2011年

作者： Guojun Gan

ISBN: (数字)9781439862247

ISBN: (纸本)9781439862230

关键词： Cluster analysis C++ (Computer program language)

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data clustering in C++ Har/Cdr

引用

丛书名： chapman & hall/crc data mining and knowledge discovery series

2011年

作者： Gan Guojun

ISBN: (纸本)9781439862230

data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the same group are similar and objects in different groups are quite distinct. Thousands of theoretical papers and a number of books on data clustering have been published over the past 50 years. However, few books exist to teach people how to implement data clustering algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. Using object-oriented design and programming techniques, data Clustering in C++ exploits the commonalities of all data clustering algorithms to create a flexible set of reusable classes that simplifies the implementation of any data clustering algorithm. Readers can follow the development of the base data clustering classes and several popular data clustering algorithms. Additional topics such as data pre-processing, data visualization, cluster visualization, and cluster interpretation are briefly covered. This book is divided into three parts-- data Clustering and C++ Preliminaries: A review of basic concepts of data clustering, the unified modeling language, object-oriented programming in C++, and design patterns A C++ data Clustering Framework: The development of data clustering base classes data Clustering Algorithms: The implementation of several popular data clustering algorithms A key to learning a clustering algorithm is to implement and experiment the clustering algorithm. Complete listings of classes, examples, unit test cases, and GNU configuration files are included in the appendices of this book as well as in the CD-ROM of the book. The only requirements to compile the code are a modern C++ compiler and the Boost C++ libraries.

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