  One of important aims of astronomical data mining is to systematically search
for specific rare objects in a massive spectral dataset, given a small fraction
of identified samples with the same type. Most existing methods are mainly
based on binary classification, which usually suffer from uncompleteness when
the known samples are too few. While, rank-based methods would provide good
solutions for such case. After investigating several algorithms, a method
combining bipartite ranking model with bootstrap aggregating techniques was
developed in this paper. The method was applied in searching for carbon stars
in the spectral data of Sloan Digital Sky Survey (SDSS) DR10, and compared with
several other popular methods used in data mining. Experimental results
validate that the proposed method is not only the most effective but also less
time consuming among these competitors automatically searching for rare spectra
in a large but unlabelled dataset.128
