PhD, James Cook University
The objective of the of the study is to find variable pulsating stars stars with temperature ranges between 8000 and 18000 Kelvin. Data will be generated from star databases such as Kepler and OGLE. Machine Learning algorithms and Data Mining will be applied to classify stars. In addition to classifications, astrophysical studies will be conducted. By studying pulsating stars, we get an insight on the interior structure of stars and we can deduce "What drives oscillations in stars".
Abstract: This paper describes a clustering method for unsupervised classification of objects in large data sets. The new methodology combines the mixture likelihood approach with a sampling and subsampling strategy in order to cluster large data sets efficiently. This sampling strategy can be applied to a large variety of data mining methods to allow them to be used on very large data sets. The method is applied to the problem of automated star/galaxy classification for digital sky data and is tested using a sample from the Digitized Palomar Sky Survey (DPOSS) data. The method is quick and reliable and produces classifications comparable to previous work on these data using supervised clustering.
Pub.: 01 Apr '03, Pinned: 28 Jul '17
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