02998cam a22002774i 4500
130619s2014 flua b 001 0 eng
9781439860847
006.312
P7
Practical graph mining with R
Boca Raton
CRC Press
2014
xxi, 473 p.
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
284499
Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs.Hands-On Application of Graph Data MiningEach chapter in the book focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through applications using real data sets, the book demonstrates how computational techniques can help solve real-world problems. The applications covered include network intrusion detection, tumor cell diagnostics, face recognition, predictive toxicology, mining metabolic and protein-protein interaction networks, and community detection in social networks.Develops Intuition through Easy-to-Follow Examples and Rigorous Mathematical FoundationsEvery algorithm and example is accompanied with R code. This allows readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice. The text also gives a rigorous, formal explanation of the underlying mathematics of each technique.Makes Graph Mining Accessible to Various Levels of ExpertiseAssuming no prior knowledge of mathematics or data mining, this self-contained book is accessible to students, researchers, and practitioners of graph data mining. It is suitable as a primary textbook for graph mining or as a supplement to a standard data mining course. It can also be used as a reference for researchers in computer, information, and computational science as well as a handy guide for data analytics practitioners.
Data mining - Graphic methods
284500
Data visualization - Data processing
284501
R (Computer program language)
284502
Business and Economics - Statistics
284503
Computers - Database Management - Data Mining
284504
Computers - Machine theory
284505
Samatova, Nagiza F.
Editor
284506
Hendrix, William
Editor
284507
Jenkins, John
Editor
284508
Padmanabhan, Kanchana
Editor
284509
Chakraborty, Arpan
Editor
284510
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247736
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Slot 105 (0 Floor, West Wing)
2014-04-08
Pustak
4283.14
Slot 105 (0 Floor, West Wing)
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006.312 P7
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2019-12-15
2019-05-24
5353.93
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