Contrast Data Mining (eBook, PDF)

Concepts, Algorithms, and Applications
Redaktion: Dong, Guozhu; Bailey, James

• Format: PDF

Produktbeschreibung

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

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 434
• Erscheinungstermin: 19. April 2016
• Englisch
• ISBN-13: 9781439854334
• Artikelnr.: 39453577

Autorenporträt

Guozhu Dong is a professor at Wright State University. A senior member of the IEEE and ACM, Dr. Dong holds four U.S. patents and has authored over 130 articles on databases, data mining, and bioinformatics; co-authored Sequence Data Mining; and co-edited Contrast Data Mining and Applications. His research focuses on contrast/emerging pattern mining and applications as well as first-order incremental view maintenance. He has a PhD in computer science from the University of Southern California. James Bailey is an Australian Research Council Future Fellow in the Department of Computing and Information Systems at the University of Melbourne. Dr. Bailey has authored over 100 articles and is an associate editor of IEEE Transactions on Knowledge and Data Engineering and Knowledge and Information Systems: An International Journal. His research focuses on fundamental topics in data mining and machine learning, such as contrast pattern mining and data clustering, as well as application aspects in areas, including health informatics and bioinformatics. He has a PhD in computer science from the University of Melbourne.

Inhaltsangabe

Preliminaries and Statistical Contrast Measures. Contrast Mining
Algorithms. Generalized Contrasts, Emerging Data Cubes, and Rough Sets.
Contrast Mining for Classification and Clustering. Contrast Mining for
Bioinformatics and Chemoinformatics. Contrast Mining for Special Domains.
Survey of Other Papers. Bibliography. Index.