Data mining is a powerful tool to obtain new knowledge and make
scientific discoveries. One of key problems in astronomy is the
classification of astronomical objects based on their observational
parameters. Main goal of the current presentation is a description
of method and results of data mining application to automatic
classification of eclipsing binaries.
The method is based on the data from a thousand classified
systems and allows for the classification of a given system based
on a set of observational parameters, even if the set is incomplete.
The procedure is applied to large catalogues of eclipsing variables,
including those obtained as by-products of microlensing surveys
(OGLE, MACHO, ASAS-3).
Also, after careful analysis of Data Mining methods and approaches of
their incorporation into the Virtual Observatory infrastructure
(AstroGrid) the CEA-application has been developed under the
name Ensembled Weka that provides for various variants of
composition of ensembles of algorithms. Ensembled Weka solves a
problem defined by a problem description file. The design gives
also an ability of hierarchical data analysis.
Ensembled Weka has been checked by solving of eclipsing binaries