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Practical Graph Mining With R, Hardcover by Samatova, Nagiza F. (EDT); Padman...
- Item No : 388313308403
- Condition : Like New
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- Seller : greatbookprices1
- Current Bid : US $126.08
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* Item Description
- Practical Graph Mining With R, Hardcover by Samatova, Nagiza F. (EDT); Padmanabhan, Kanchana (INT); Hendrix, William S. (INT), ISBN 143986084X, ISBN-13 9781439860847, Like New Used, Free shipping in the US
"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 Application of Graph Data MiningEach chapter in th focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through applications using real data sets, th 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 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 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"--
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