SOM ensemble for unsupervised outlier analysis. Application to outlier identification in the Gaia astronomical survey

Authors:
Diego Fustes;Carlos Dafonte;Bernardino Arcay;Minia Manteiga;Kester Smith;Antonella Vallenari;Xavier Luri
Affiliations:
Fac. Informática, Universidade da Coruña, Campus de Elviña, 15071 A Coruña, Spain;Fac. Informática, Universidade da Coruña, Campus de Elviña, 15071 A Coruña, Spain;Fac. Informática, Universidade da Coruña, Campus de Elviña, 15071 A Coruña, Spain;Fac. Informática, Universidade da Coruña, Campus de Elviña, 15071 A Coruña, Spain;Max Planck Institute for Astronomy, Königstuhl 17, Heidelberg 69117, Germany;INAF, Osservatorio Astronomico di Padova, Vicolo Osservatorio 5, Padova, Italy;Dept. Astronomia i Meteorologia ICCUB-IEEC, Martí i Franquès 1, Barcelona, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

Citing 8
Cited 0

Analyzing Outliers Cautiously

IEEE Transactions on Knowledge and Data Engineering
Ensemble Methods in Machine Learning

MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Mining massive document collections by the WEBSOM method

Information Sciences: an International Journal - Special issue: Soft computing data mining
Clustering of time series data-a survey

Pattern Recognition
HSC: A multi-resolution clustering strategy in Self-Organizing Maps applied to astronomical observations

Applied Soft Computing
A survey of fuzzy clustering algorithms for pattern recognition. I

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Paper: Identifying the measurement noise in glaucomatous testing: An artificial neural network approach

Artificial Intelligence in Medicine
Survey of clustering algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	12.05

Visualization

Abstract

Gaia is an ESA cornerstone astronomical mission that will observe with unprecedented precision positions, distances, space motions, and many physical properties of more than one billion objects in our Galaxy and beyond. It will observe all objects in the sky in the visible magnitude range from 6 to 20, up to approximately 10^9 sources. An international scientific consortium, the Gaia Data Processing and Analysis Consortium (Gaia DPAC), has organized itself in several coordination units, with the aim, among others, of addressing the work of classifying the observed astronomical sources, using both supervised and unsupervised classification algorithms. This work focuses on the analysis of classification outliers by means of unsupervised classification. We present a novel method to combine SOMs trained with independent features that are calculated from spectrophotometry. The method as described here can help to improve the models used for the supervised classification of astronomical sources. Furthermore, it allows for data exploration and knowledge discovery in huge astronomical databases such as the upcoming Gaia mission.