A subspace filter supporting the discovery of small clusters in very noisy datasets

Feature selection becomes crucial when exploring high-dimensional datasets via clustering, because it is unlikely that the data groups jointly in all dimensions but clustering algorithms treat all attributes equally. A new subspace filter approach is presented that is capable of coping with the difficult situation of finding small clusters embedded in a very noisy environment (more noise than clustering data), which is not mislead by dense, high-dimensional spots caused by density fluctuations of single attributes. Experimental evaluation on artificial and real datasets demonstrate good performance and high efficiency.