Bøger i Chapman & Hall/CRC Data Mining and Knowledge Discovery Series serien

Focusing on data mining mechanics and applications, this book explores some of the most common matrix decompositions, including singular value, semidiscrete, independent component analysis, non-negative matrix factorization, and tensors. It also discusses several important theoretical and algorithmic problems of matrix decompositions.

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.

Covers privacy and anonymity for data mining applications. This book presents novel application domains, such as data mining of biomedical and healthcare data. It addresses spatial, temporal, and spatio-temporal data as well graphs, links, and social networks. It details privacy-aware data publishing and mining of data streams.