Format:
1 Online-Ressource (xi, 467 Seiten)
,
Diagramme
Edition:
Second edition
ISBN:
9781139924801
Content:
Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction
Content:
Data mining -- MapReduce and the new software stack -- Finding similar items -- Mining data streams -- Link analysis -- Frequent itemsets -- Clustering -- Advertising on the Web -- Recommendation systems -- Mining social-network graphs -- Dimensionality reduction -- Large-scale machine learning
Additional Edition:
ISBN 9781107077232
Additional Edition:
Erscheint auch als Druck-Ausgabe Leskovec, Jure Mining of massive datasets Cambridge : Cambridge University Press, 2014 ISBN 9781107077232
Language:
English
Subjects:
Computer Science
Keywords:
Data Mining
DOI:
10.1017/CBO9781139924801
Author information:
Ullman, Jeffrey D. 1942-
