Author(s) Name:  Michael J. Way, Jeffrey D. Scargle, Kamal M. Ali, Ashok N. Srivastava

About the Book:

   Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science.
   The book-s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications.
   With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Table of Contents

Foundational Issues

  Classification in Astronomy: Past and Present
  Searching the Heavens: Astronomy, Computation, Statistics, Data Mining, and Philosophy
  Probability and Statistics in Astronomical Machine Learning and Data Mining

Source Identification

  Automated Science Processing for the Fermi Large Area Telescope
  Data Mining and Machine Learning in Time-Domain Discovery and Classification
  Cross-Identification of Sources: Theory and Practice
  The Sky Pixelization for CMB Mapping
  Future Sky Surveys: New Discovery Frontiers

Classification

  Galaxy Zoo: Morphological Classification and Citizen Science
  The Utilization of Classifications in High-Energy Astrophysics Experiments
  Database-Driven Analyses of Astronomical Spectra

Signal Processing (Time-Series) Analysis

  Planet Detection: The Kepler Mission
  Classification of Variable Objects in Massive Sky Monitoring Surveys
  Gravitational Wave Astronomy

The Largest Data Sets

  Virtual Observatory and Distributed Data Mining
  Multitree Algorithms for Large-Scale Astrostatistics

Machine Learning Methods

  Time–Frequency Learning Machines for Nonstationarity Detection Using Surrogates
  Classification
  On the Shoulders of Gauss, Bessel, and Poisson: Links, Chunks, Spheres, and Conditional Models
  Ensemble Methods: A Review
  Parallel and Distributed Data Mining for Astronomy Applications
  Pattern Recognition in Time Series
  Randomized Algorithms for Matrices and Data

ISBN:  9781439841730

Publisher:  Chapman and Hall/CRC

Year of Publication:  2012

Book Link:  Home Page Url