DAVID J.HAND, HEIKKI MANNILS, PADHRAIC SMYTH – PRINCIPLES OF DATA MINING

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets. Different aspects of data mining have been addressed by different disciplines. The book blends the contributions of information science, computer science, and statistics. The first foundation provides a primer on the principles of data mining. The presentation emphasizes intuition. The second section shows how data mining algorithms are constructed to solve specific problems in a principled manner. Neural networks, classical statistical models, and local memory-based models are some of the models covered. The third section shows how the analysis fits together when applied to real-world data mining problems. Data preprocessing and how to handle missing data are topics.

David J. Hand, Heikki Mannils, Padhraic Smyth, Principles of Data Mining, download.