  Astronomy is undergoing through a methodological revolution triggered by an
unprecedented wealth of complex and accurate data. The new panchromatic,
synoptic sky surveys require advanced tools for discovering patterns and trends
hidden behind data which are both complex and of high dimensionality. We
present DAMEWARE (DAta Mining & Exploration Web Application REsource): a
general purpose, web-based, distributed data mining environment developed for
the exploration of large datasets, and finely tuned for astronomical
applications. By means of graphical user interfaces, it allows the user to
perform classification, regression or clustering tasks with machine learning
methods. Salient features of DAMEWARE include its capability to work on large
datasets with minimal human intervention, and to deal with a wide variety of
real problems such as the classification of globular clusters in the galaxy
NGC1399, the evaluation of photometric redshifts and, finally, the
identification of candidate Active Galactic Nuclei in multiband photometric
surveys. In all these applications, DAMEWARE allowed to achieve better results
than those attained with more traditional methods. With the aim of providing
potential users with all needed information, in this paper we briefly describe
the technological background of DAMEWARE, give a short introduction to some
relevant aspects of data mining, followed by a summary of some science cases
and, finally, we provide a detailed description of a template use case.
