Statistical Analysis & Data Mining publishes special issue

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  • Author: Statistics Views
  • Date: 16 May 2013

The NASA Conference on Intelligent Data Understanding (CIDU) is application-oriented with a focus on Earth & Environmental Systems, Space Science, and Aerospace & Engineering Systems. The conference was held on 19th–21st October, 2011, in Mountain View, CA, at the Computer History Museum. Statistical Analysis & Data Mining has just published a special issue of papers presented at the conference.

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The CIDU 2011 Program Committee solicited theme-oriented papers that help advance one of the aforementioned applications through machine-learning or data-mining techniques. Papers were invited that include a clear link between the domain (application area) and machine learning and data mining.

The papers of this year cover a significant number of application areas and also new algorithmic developments. Of the papers accepted at the conference seven were selected for publication in this Special Issue and cover a wide range of domains and application areas including:

  • Novel methods to discover dynamic dipoles in climate data (Kawale et al.).
  • New approaches to discover strongly nonlinear dynamics based on climatic time series (Giannakis et al.).
  • A novelty detection algorithm that uses adaptive eigenbases to combine domain knowledge and the statistical behavior of the data to detect novel signals (Thompson et al.).
  • A technique that uses sparse inverse Gaussian process regression to discover relationships in climate networks (Das et al.).
  • The use of sparse machine-learning techniques to discover topics in large text corporate (El Ghaoui et al.).
  • A new approach for analyzing multidimensional text databases based on developing micro-text clusters (Zhang et al.).
  • A methodology for predicting the remaining useful life of an individual component which is subjected to a time-varying environment (Bian et al.).

These topics are sure to provide the reader with an understanding of the way machine-learning and data-mining techniques can be applied to a broad set of domains.

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