Practical Graph Mining with R

Assuming no prior knowledge of mathematics or data mining, this self-contained book presents a "do-it-yourself" approach to extracting interesting patterns from graph data. Each chapter focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through many applications, the book demonstrates how computational techniques can help solve real-world problems. Every algorithm and example is accompanied with R code, allowing readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice.

Sommario

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.

Info autore

Nagiza F. Samatova is an associate professor of computer science at North Carolina State University and a senior research scientist at Oak Ridge National Laboratory.

Dettagli sul prodotto

• Editore: Taylor & Francis Ltd.
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 01.01.2013
• Pagine: 495
• Dimensioni: 162 mm x 238 mm x 31 mm
• Peso: 832 g
• Illustrazioni: 168 black & white illustrations, 45 black & white tables
• Serie: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
• Categoria: Scienze sociali, diritto, economia > Economia > Tematiche generali, enciclopedie