Knowledge Discovery in Large Data Sets

Journal Article · Fri Dec 05 00:00:00 EST 2008 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.3059044· OSTI ID:21254925

Simas, Tiago; Silva, Gabriel; Miranda, Bruno; Ribeiro, Rita ^[1]; Moitinho, Andre ^[2]

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1. Uninova/CA3, Universidade Nova de Lisboa (Portugal)
2. SIM, Universidade de Lisboa (Portugal)

In this work we briefly address the problem of unsupervised classification on large datasets, magnitude around 100,000,000 objects. The objects are variable objects, which are around 10% of the 1,000,000,000 astronomical objects that will be collected by GAIA/ESA mission. We tested unsupervised classification algorithms on known datasets such as OGLE and Hipparcos catalogs. Moreover, we are building several templates to represent the main classes of variable objects as well as new classes to build a synthetic dataset of this dimension. In the future we will run the GAIA satellite scanning law on these templates to obtain a testable large dataset.

• OSTI ID: 21254925
• Journal Information: AIP Conference Proceedings, Vol. 1082, Issue 1; Conference: International conference on classification and discovery in large astronomical surveys, Ringberg Castle (Germany), 14-17 Oct 2008; Other Information: DOI: 10.1063/1.3059044; (c) 2008 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ALGORITHMS
CATALOGS
CLASSIFICATION
DATA BASE MANAGEMENT
KNOWLEDGE BASE
KNOWLEDGE MANAGEMENT
MASS SPECTROSCOPY
SATELLITES