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Data mining techniques must be developed and applied to analyse the largepublic data bases containing hundreds to thousands of millions entries. The aimof this study is to develop methods for locating previously unknown stellarclusters from the UKIDSS Galactic Plane Survey catalogue data. The clustercandidates are computationally searched from pre-filtered catalogue data usinga method that fits a mixture model of Gaussian densities and background noiseusing the Expectation Maximization algorithm. The catalogue data contains asignificant number of false sources clustered around bright stars. A largefraction of these artefacts were automatically filtered out before or duringthe cluster search. The UKIDSS data reduction pipeline tends to classifymarginally resolved stellar pairs and objects seen against variable surfacebrightness as extended objects (or "galaxies" in the archive parlance). 10% or66 x 10^6 of the sources in the UKIDSS GPS catalogue brighter than 17magnitudes in the K band are classified as "galaxies". Young embedded clusterscreate variable NIR surface brightness because the gas/dust clouds in whichthey were formed scatters the light from the cluster members. Such clustersappear therefore as clusters of "galaxies" in the catalogue and can be foundusing only a subset of the catalogue data. The detected "galaxy clusters" werefinally screened visually to eliminate the remaining false detections due todata artefacts. Besides the embedded clusters the search also located locationsof non clustered embedded star formation. The search covered an area of 1302square degrees and 137 previously unknown cluster candidates and 30 previouslyunknown sites of star formation were found.

Mining the UKIDSS GPS: star formation and embedded clusters