Practical graph mining with R
著者
書誌事項
Practical graph mining with R
（Chapman & Hall/CRC data mining and knowledge discovery series）
CRC Press, c2014
- : hardback
大学図書館所蔵 全7件
注記
"A Chapman & Hall book."
Includes bibliographical references and index
内容説明・目次
内容説明
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 Mining
Each 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 Foundations
Every 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 Expertise
Assuming 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.
目次
Introduction. An Introduction to Graph Theory. An Introduction to R. An Introduction to Kernel Functions. Link Analysis. Graph-Based Proximity Measures. Frequent Subgraph Mining. Cluster Analysis. Classification. Dimensionality Reduction. Graph-Based Anomaly Detection. Performance Metrics for Graph Mining Tasks. Introduction to Parallel Graph Mining. Index.
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