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Frequent Pattern Mining [electronic resource] / edited by Charu C. Aggarwal, Jiawei Han.

Contributor(s): Aggarwal, Charu C [editor.] | Han, Jiawei [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XIX, 471 p. 83 illus., 19 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319078212.Subject(s): Computer science | Database management | Data mining | Artificial intelligence | Pattern recognition | Biometrics (Biology) | Computer Science | Data Mining and Knowledge Discovery | Database Management | Artificial Intelligence (incl. Robotics) | Pattern Recognition | BiometricsAdditional physical formats: Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
Contents:
An Introduction to Frequent Pattern Mining -- Frequent Pattern Mining Algorithms: A Survey -- Pattern-growth Methods -- Mining Long Patterns -- Interesting Patterns -- Negative Association Rules -- Constraint-based Pattern Mining -- Mining and Using Sets of Patterns through Compression -- Frequent Pattern Mining in Data Streams -- Big Data Frequent Pattern Mining -- Sequential Pattern Mining -- Spatiotemporal Pattern Mining: Algorithms and Applications -- Mining Graph Patterns -- Uncertain Frequent Pattern Mining -- Privacy in Association Rule Mining -- Frequent Pattern Mining Algorithms for Data Clustering -- Supervised Pattern Mining and Applications to Classification -- Applications of Frequent Pattern Mining.
In: Springer eBooksSummary: 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 students in computer science, researchers and practitioners from industry will find this book an invaluable reference.
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An Introduction to Frequent Pattern Mining -- Frequent Pattern Mining Algorithms: A Survey -- Pattern-growth Methods -- Mining Long Patterns -- Interesting Patterns -- Negative Association Rules -- Constraint-based Pattern Mining -- Mining and Using Sets of Patterns through Compression -- Frequent Pattern Mining in Data Streams -- Big Data Frequent Pattern Mining -- Sequential Pattern Mining -- Spatiotemporal Pattern Mining: Algorithms and Applications -- Mining Graph Patterns -- Uncertain Frequent Pattern Mining -- Privacy in Association Rule Mining -- Frequent Pattern Mining Algorithms for Data Clustering -- Supervised Pattern Mining and Applications to Classification -- Applications of Frequent Pattern Mining.

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 students in computer science, researchers and practitioners from industry will find this book an invaluable reference.

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