Guozhu Dong – författare
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16 produkter
16 produkter
Häftad, Engelska, 2020
704 kr
Skickas inom 10-15 vardagar
Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features.The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively.This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.
Inbunden, Engelska, 2007
1 123 kr
Skickas inom 10-15 vardagar
This book provides balanced coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This volume fills in the gap, allowing readers to access the state-of-the-art results in one place. Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering.
E-bok
PDF, Engelska, 20071 367 kr
Läs direkt efter köp
This book provides balanced coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This volume fills in the gap, allowing readers to access the state-of-the-art results in one place. Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering.
E-bok
Engelska, 2016942 kr
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A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life ProblemsContrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and
Inbunden, Engelska, 2018
1 678 kr
Skickas inom 10-15 vardagar
Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features.The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively.This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.
E-bok
Engelska, 2018779 kr
Läs direkt efter köp
Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation.
The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features.
The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively.
This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.
E-bok
PDF, Engelska, 2018779 kr
Läs direkt efter köp
Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation.
The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features.
The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively.
This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.
Inbunden, Engelska, 2012
1 750 kr
Skickas inom 10-15 vardagar
A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life ProblemsContrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and other fields. The book not only presents concepts and techniques for contrast data mining, but also explores the use of contrast mining to solve challenging problems in various scientific, medical, and business domains. Learn from Real Case Studies of Contrast Mining ApplicationsIn this volume, researchers from around the world specializing in architecture engineering, bioinformatics, computer science, medicine, and systems engineering focus on the mining and use of contrast patterns. They demonstrate many useful and powerful capabilities of a variety of contrast mining techniques and algorithms, including tree-based structures, zero-suppressed binary decision diagrams, data cube representations, and clustering algorithms. They also examine how contrast mining is used in leukemia characterization, discriminative gene transfer and microarray analysis, computational toxicology, spatial and image data classification, voting analysis, heart disease prediction, crime analysis, understanding customer behavior, genetic algorithms, and network security.
E-bok
PDF, Engelska, 2016909 kr
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A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life ProblemsContrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and
Del 33 - Advances in Database Systems
Sequence Data Mining
Häftad, Engelska, 2010
1 458 kr
Skickas inom 10-15 vardagar
This book provides balanced coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This volume fills in the gap, allowing readers to access the state-of-the-art results in one place. Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering.
Häftad, Engelska, 2019
657 kr
Skickas inom 10-15 vardagar
This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included.Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on.EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines.Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest.We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.
E-bok
PDF, Engelska, 2022870 kr
Läs direkt efter köp
This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included.Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on.EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines.Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest.We believe that many effective ways to exploit group differences'' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.
Häftad, Engelska, 2003
565 kr
Skickas inom 10-15 vardagar
With advances in the Internet and technologies around the World-Wide Web, research on design, implementation, and management of Internet- and W- based information systems has become increasingly important. As more and more information of diverse type becomes available on the Internet and Web, query and retrieval as well as the management of information over the Internet become more complex and extremely di?cult. Novel approaches to develop and manage Internet and Web information systems are in high demand. Following the successful conferences in 2000, 2001 and 2002, WAIM 2003 continued to provide a forum for researchers, professionals, and industrial practitioners from around the world to share their rapidly evolving knowledge and to report on new advances in Web-based information systems. WAIM 2003 received an overwhelming 258 submissions from Australia, Canada, China, Denmark, France, Germany, Greece, Hong Kong, Japan, South Korea, Pakistan, Singapore, Sweden, Switzerland, Taiwan, Thailand, UK, USA, and Vietnam. Through careful review by the program committee, 30 papers were selected as regular papers, and 16 papers as short papers. As indicated by these numbers, WAIM 2003 is extremely selective: 11 and 17 areas, respectively, including text management, data mining, information ?ltering, moving objects, views, bioinformatics, Web and XML, multimedia, peer-to-peer systems, service networks, time-series streams, and ontologies. Two invited talks by Sushil Ja- dia (George Mason University, USA) and Beng Chin Ooi (National University of Singapore) were on access control models and peer-to-peer systems.
E-bok
PDF, Engelska, 2003714 kr
Läs direkt efter köp
With advances in the Internet and technologies around the World-Wide Web, research on design, implementation, and management of Internet- and W- based information systems has become increasingly important. As more and more information of diverse type becomes available on the Internet and Web, query and retrieval as well as the management of information over the Internet become more complex and extremely di?cult. Novel approaches to develop and manage Internet and Web information systems are in high demand. Following the successful conferences in 2000, 2001 and 2002, WAIM 2003 continued to provide a forum for researchers, professionals, and industrial practitioners from around the world to share their rapidly evolving knowledge and to report on new advances in Web-based information systems. WAIM 2003 received an overwhelming 258 submissions from Australia, Canada, China, Denmark, France, Germany, Greece, Hong Kong, Japan, South Korea, Pakistan, Singapore, Sweden, Switzerland, Taiwan, Thailand, UK, USA, and Vietnam. Through careful review by the program committee, 30 papers were selected as regular papers, and 16 papers as short papers. As indicated by these numbers, WAIM 2003 is extremely selective: 11 and 17 areas, respectively, including text management, data mining, information ?ltering, moving objects, views, bioinformatics, Web and XML, multimedia, peer-to-peer systems, service networks, time-series streams, and ontologies. Two invited talks by Sushil Ja- dia (George Mason University, USA) and Beng Chin Ooi (National University of Singapore) were on access control models and peer-to-peer systems.
1 125 kr
Skickas inom 10-15 vardagar
The rapid prevalence of Web applications requires new technologies for the - sign, implementation and management of Web-based information systems, and for the management and analysis of information on the Web. The joint AP- Web/WAIM 2007 conference, combining the traditions of APWeb and WAIM conferences,wasaninternationalforumforresearchers,practitioners,developers and users to share and exchange cutting-edge ideas, results, experience, te- niques and tools in connection with all aspects of Web data management. The conference drew together original research and industrial papers on the theory, design and implementation of Web-based information systems and on the m- agement and analysis of information on the Web. The conference was held in the beautiful mountain area of Huang Shan (Yellow Mountains) — the only dual World Heritage listed area in China for its astonishing natural beauty and richandwell-preservedculture. Theseproceedingscollectedthe technicalpapers selected for presentation at the conference, held at Huang Shan, June 16–18, 2007. In response to the call for papers, the Program Committee received 554 full-paper submissions from North America, South America, Europe, Asia, and Oceania. Each submitted paper underwent a rigorous review by three in- pendent referees, with detailed review reports. Finally, 47 full research papers and 36 short research papers were accepted, from Austria, Australia, Canada, China,Cyprus,Greece,HongKong,Japan,Korea,Singapore,Taiwan,andUSA, representing a competitive acceptance rate of 15%.
E-bok
PDF, Engelska, 20071 408 kr
Läs direkt efter köp
The rapid prevalence of Web applications requires new technologies for the - sign, implementation and management of Web-based information systems, and for the management and analysis of information on the Web. The joint AP- Web/WAIM 2007 conference, combining the traditions of APWeb and WAIM conferences,wasaninternationalforumforresearchers,practitioners,developers and users to share and exchange cutting-edge ideas, results, experience, te- niques and tools in connection with all aspects of Web data management. The conference drew together original research and industrial papers on the theory, design and implementation of Web-based information systems and on the m- agement and analysis of information on the Web. The conference was held in the beautiful mountain area of Huang Shan (Yellow Mountains) — the only dual World Heritage listed area in China for its astonishing natural beauty and richandwell-preservedculture. Theseproceedingscollectedthe technicalpapers selected for presentation at the conference, held at Huang Shan, June 16–18, 2007. In response to the call for papers, the Program Committee received 554 full-paper submissions from North America, South America, Europe, Asia, and Oceania. Each submitted paper underwent a rigorous review by three in- pendent referees, with detailed review reports. Finally, 47 full research papers and 36 short research papers were accepted, from Austria, Australia, Canada, China,Cyprus,Greece,HongKong,Japan,Korea,Singapore,Taiwan,andUSA, representing a competitive acceptance rate of 15%.