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 ch...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Aggarwal, Charu C. (Επιμελητής έκδοσης), Han, Jiawei (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2014.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |a Frequent Pattern Mining  |h [electronic resource] /  |c edited by Charu C. Aggarwal, Jiawei Han. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2014. 
300 |a XIX, 471 p. 83 illus., 19 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
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505 0 |a 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. 
520 |a 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. 
650 0 |a Computer science. 
650 0 |a Database management. 
650 0 |a Data mining. 
650 0 |a Artificial intelligence. 
650 0 |a Pattern recognition. 
650 0 |a Biometrics (Biology). 
650 1 4 |a Computer Science. 
650 2 4 |a Data Mining and Knowledge Discovery. 
650 2 4 |a Database Management. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Pattern Recognition. 
650 2 4 |a Biometrics. 
700 1 |a Aggarwal, Charu C.  |e editor. 
700 1 |a Han, Jiawei.  |e editor. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783319078205 
856 4 0 |u http://dx.doi.org/10.1007/978-3-319-07821-2  |z Full Text via HEAL-Link 
912 |a ZDB-2-SCS 
950 |a Computer Science (Springer-11645)