A new approach to distributed large-scale datamining, ser.ice-oriented knowledgediscovery extracts useful knowledge from today's often unmanageable volumes of data by exploiting datamining and machine learning ...
详细信息
ISBN:
(数字)9780429109911
ISBN:
(纸本)9781439875315
A new approach to distributed large-scale datamining, ser.ice-oriented knowledgediscovery extracts useful knowledge from today's often unmanageable volumes of data by exploiting datamining and machine learning distributed models and techniques in ser.ice-oriented infrastructures. ser.ice-Oriented Distributed knowledgediscovery presents techniqu
Advances in Machine Learning and datamining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art mach...
详细信息
ISBN:
(数字)9781439841747
ISBN:
(纸本)9781138199309
Advances in Machine Learning and datamining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and datamining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines
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...
详细信息
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 datamining presents a variety of approaches to
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 di...
详细信息
ISBN:
(数字)9781439862247
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,
Machine Learning and knowledgediscovery for Engineering Systems Health Management presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events i...
ISBN:
(纸本)9781439841785;1439841780
Machine Learning and knowledgediscovery 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 obser.ed 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 knowledgediscovery 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.
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 fea...
详细信息
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.
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 di...
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.
An emerging topic in software engineering and datamining, specification mining tackles software maintenance and reliability issues that cost economies billions of dollars each year. The first unified reference on the...
ISBN:
(纸本)9781439806265;1439806268
An emerging topic in software engineering and datamining, specification mining tackles software maintenance and reliability issues that cost economies billions of dollars each year. The first unified reference on the subject, mining Software Specifications: Methodologies and Applications describes recent approaches for mining specifications of software systems. Experts in the field illustrate how to apply state-of-the-art datamining and machine learning techniques to address software engineering concerns. In the first set of chapters, the book introduces a number of studies on mining finite state machines that employ techniques, such as grammar inference, partial order mining, source code model checking, abstract interpretation, and more. The remaining chapters present research on mining temporal rules/patterns, covering techniques that include path-aware static program analyses, lightweight rule/pattern mining, statistical analysis, and other interesting approaches. Throughout the book, the authors discuss how to employ dynamic analysis, static analysis, and combinations of both to mine software specifications. According to the US National Institute of Standards and Technology in 2002, software bugs have cost the US economy 59.5 billion dollars a year. This volume shows how specification mining can help find bugs and improve program understanding, thereby reducing unnecessary financial losses. The book encourages the industry adoption of specification mining techniques and the assimilation of these techniques in standard integrated development environments (IDEs).
A culmination of the authors' years of extensive research on this topic, Relational data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. ...
详细信息
ISBN:
(数字)9781420072624
ISBN:
(纸本)9781420072617
A culmination of the authors' years of extensive research on this topic, Relational data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems. After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering: Clustering on bi-type heterogeneous relational data Multi-type heterogeneous relational data Homogeneous relational data clustering Clustering on the most general case of relational data Individual relational clustering framework Recent research on evolutionary clustering This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.
Temporal datamining deals with the harvesting of useful information from temporal data. New initiatives in health care and business organizations have increased the importance of temporal information in data *** basi...
详细信息
ISBN:
(数字)9781420089776
ISBN:
(纸本)9781420089769
Temporal datamining deals with the harvesting of useful information from temporal data. New initiatives in health care and business organizations have increased the importance of temporal information in data *** basic datamining concepts to state-of-the-art advances, Temporal datamining covers the theory of this subject as well as its app
暂无评论