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.
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.
This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal...
Graph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world applications in fields such as bioinformatics, traffic...
Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing,...
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