Introduction -- Measurement and data -- Visualizing and exploring data -- Data analysis and uncertainty -- A systematic overview of data mining algorithms -- Models and patterns -- Score functions for data mining algorithms -- Search and optimization methods -- Descriptive modeling -- Predictive modeling for classification -- Predictive modeling for regression -- Data organization and databases -- Finding patterns and rules -- Retrieval by content.

The rapid growth and integration of databases provides scientists, engineers, and business people with a vast new resource that can be analyzed to make scientific discoveries, optimize industrial systems, and uncover financially valuable patterns. To undertake these large data mining projects, researchers and practitioners have adopted established algorithms from statistics, machine learning, neural networks, and databases and have also developed new methods targeted at large data mining problems.Principles of Data Mining with its unique blend of inputs from information science, computer science, and statistics provides practitioners and students with an introduction to the wide range of algorithms and methodologies in this exciting area.