Books by Charu C. Aggarwal

These methods are discussed in Chapters 1through 5. Inductive Learning Methods: These methods start with examples and use statistical methods in order to arrive at hypotheses.

This textbook introduces linear algebra and optimization in the context of machine learning. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra.

Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines.Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks.

This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification.

This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues.