With contributions from leading experts in the data mining community, this volume provides a comprehensive look at the next generation of data mining challenges and applications. It explores the rise of data mining as a research science and the preeminent role it will play in the future. The book focuses on the use of data mining and knowledge discovery tools and techniques in a variety of areas, including e-science, engineering, social science, finance, and medicine. It looks at data mining on the web in search engines and social computing, examines data mining issues in security and...
With contributions from leading experts in the data mining community, this volume provides a comprehensive look at the next generation of data mining ...
This collection addresses the challenges to be found in knowledge discovery and data mining from geographic databases. Data mining is the interrogation of large databases using efficient computational methods.
This collection addresses the challenges to be found in knowledge discovery and data mining from geographic databases. Data mining is the interrogatio...
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge....
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now co...
With the recent ?ourishing research activities on Web search and mining, social networkanalysis, informationnetworkanalysis, informationretrieval, linkana- sis, andstructuraldatamining, researchonlinkmininghasbeenrapidlygrowing, forminganew?eldofdatamining. Traditionaldataminingfocuseson"?at"or"isolated"datainwhicheachdata objectisrepresentedasanindependentattributevector. However, manyreal-world data sets are inter-connected, much richer in structure, involving objects of h- erogeneoustypesandcomplexlinks. Hence, thestudyoflinkminingwillhavea...
With the recent ?ourishing research activities on Web search and mining, social networkanalysis, informationnetworkanalysis, informationretrieval, lin...
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...
Machine Learning and Knowledge Discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques ...
This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level...
This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspe...
Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more.
Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. Key topics include: Pattern Growth Methods, Frequent Pattern Mi...
