Scientific Data Mining

Technological advances are enabling scientists to collect vast amounts of data in fields such as medicine, remote sensing, astronomy, and high-energy physics. These data arise not only from experiments and observations, but also from computer simulations of complex phenomena. As a result, it has become impractical to manually analyze and understand the data. This book describes how techniques from the multi-disciplinary field of data mining can be used to address the modern problem of data overload in science and engineering domains. Starting with a survey of analysis problems in different applications, it identifies the common themes across these domains and uses them to define an end-to-end process of scientific data mining. This multi-step process includes tasks such as processing the raw image or mesh data to identify objects of interest; extracting relevant features describing the objects; detecting patterns among the objects; and displaying the patterns for validation by the scientists.

• Brings together techniques from many different disciplines which are useful in the analysis of scientific data
• Gives the reader an opportunity to benefit from solutions developed in other problem domains by surveying many different science and engineering applications
• Includes a description of software systems developed for scientific data mining and general guidelines for getting started on the analysis of massive, complex data sets