Content: Part I: Foundational Issues --
Classification in Astronomy: Past and Present / Eric Feigelson --
Searching the Heavens: Astronomy, Computation, Statistics, Data Mining, and Philosophy / Clark Glymour --
Probability and Statistics in Astronomical Machine Learning and Data Mining / Jeffrey D. Scargle --
Part II: Astronomical Applications --
Source Identification --
Automated Science Processing for the Fermi Large Area Telescope / James Chiang --
CMB Data Analysis / Paniez Paykari and Jean-Luc Starck --
Data Mining and Machine Learning in Time-Domain Discovery and Classification / Joshua S. Bloom and Joseph W. Richards --
Cross-Identification of Sources: Theory and Practice / Tamás Budavári --
The Sky Pixelization for CMB Mapping / O.V. Verkhodanov and A.G. Doroshkevich --
Future Sky Surveys: New Discovery Frontiers / J. Anthony Tyson and Kirk D. Borne --
Poisson Noise Removal in Spherical Multichannel Images: Application to Fermi Data / Jérémy Schmitt, Jean-Luc Starck, Jalal Fadili, and Seth Digel --
Classification --
Galaxy Zoo: Morphological Classification and Citizen Science / Lucy Fortson, Karen Masters / Robert Nichol, Kirk D. Borne, Edd Edmondson, Chris Lintoot, Jordan Raddick, Kevin Schawinski, and John Wallin --
The Utilization of Classifications in High-Energy Astrophysics Experiments, Bill Atwood Database-Driven Analyses of Astronomical Spectra / Jan Cami --
Weak Gravitational Lensing / Sandrine Pires, Jean-Luc Starck, Adrienne Leonard, and Alexandre Réfrégier --
Photometric Redshifts: 50 Years after 345 / Tamás Budavári --
Galaxy Clusters / Christopher J. Miller --
Signal Processing (Time-Series) Analysis --
Planet Detection: The Kepler Mission / Jon M. Jenkins, Jeffrey C. Smith, Peter Tenenbaum, Joseph D. Twicken, and Jeffrey Van Cleve --
Classification of Variable Objects in Massive Sky Monitoring Surveys / Przemek Woźniak, Lukasz Wyrzykowski, and Vasily Belokurov --
Gravitational Wave Astronomy / Lee Samuel Finn --
The Largest Data Sets Virtual Observatory and Distributed Data Mining / Kirk D. Borne --
Multitree Algorithms for Large-Scale Astrostatistics / William B. March, Arkadas Ozakin, Dongryeol Lee, Ryan Riegel, and Alexander G. Gray --
Part III: Machine Learning Methods --
Time-Frequency Learning Machines for Nonstationarity Detection Using Surrogates / Pierre Borgnat, Patrick Flandrin, Cédric Richard, André Ferrari, Hassan Amoud, and Paul Honeine --
Classification / Nikunj Oza --
On the Shoulders of Gauss, Bessel, and Poisson: Links, Chunks, Spheres, and Conditional Models / William D. Heavlin --
Data Clustering / Kiri L. Wagstaff --
Ensemble Methods: A Review / Matteo Re and Giorgio Valentini --
Parallel and Distributed Data Mining for Astronomy Applications / Kamalika Das and Kanishka Bhaduri --
Pattern Recognition in Time Series / Jessica Lin, Sheri Williamson, Kirk D. Borne, and David De Barr --
Randomized Algorithms for Matrices and Data / Michael W. Mahoney.
Abstract: ''This book provides a comprehensive overview of various data mining tools and techniques that are increasingly being used by researchers in the international astronomy community. It explores this new problem domain, discussing how it could lead to the development of entirely new algorithms. Leading contributors introduce data mining methods and then describe how the methods can be implemented into astronomy applications. The last section of the book discusses the Redshift Prediction Competition, which is an astronomy competition in the style of the Netflix Prize''--Provided by publisher