Wei Wang

Guozhu Dong

Chanjie Tang

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.

4th International Conference, WAIM 2003, Chengdu, China, August 17-19, 2003, Proceedings

Advances in Web-Age Information Management

Huan Liu

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.

Feature Engineering for Machine Learning and Data Analytics

James Bailey

A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life Problems Contrast 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 Applications In 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.

Guozhu Dong

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