The problem of mining patterns is becoming a very active research area and efficient techniques have been widely applied to problems in industry, government, and science. From the initial definition and motivated by real-applications, the problem of mining patterns not only addresses the finding of itemsets but also more and more complex patterns. Successes and New Directions in Data Mining addresses existing solutions for data mining, with particular emphasis on potential real-world applications. Capturing defining research on topics such as fuzzy set theory, clustering algorithms, semi-supervised clustering, modeling and managing data mining patterns, and sequence motif mining, this book is an indispensable resource for library collections.
Cited By
Robinson W, Akhlaghi A, Deng T and Syed A (2012). Discovery and diagnosis of behavioral transitions in patient event streams, ACM Transactions on Management Information Systems (TMIS), 3:1, (1-28), Online publication date: 1-Apr-2012.- Franken H, Seitz A, Lehmann R, Häring H, Stefan N and Zell A Inferring disease-related metabolite dependencies with a bayesian optimization algorithm Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, (62-73)
- Franken H, Lehmann R, Häring H, Fritsche A, Stefan N and Zell A Wrapper- and ensemble-based feature subset selection methods for biomarker discovery in targeted metabolomics Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics, (121-132)
- Xu S Data mining using an adaptive HONN model with hyperbolic tangent neurons Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services, (73-81)
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