Data Mining: Concepts and Techniques

TL;DR: The problems motivating subspace clustering are sketched, different definitions and usages of subspaces for clusteringare described, and exemplary algorithmic solutions are discussed.

TL;DR: This work incorporates the recency, frequency, and monetary (RFM) concept presented in the marketing literature to define the RFM sequential pattern and develops a novel algorithm for generating allRFM sequential patterns from customers' purchasing data.

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.

TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?

TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.

TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.