Abstract: This paper studied how to construct cluster ensembles for high dimensional data and proposed a new ensemble constructor. ho ameliorate the effect caused by high dimensionality, the proposed method used Locality Preserving Projections(LPP) to reduce the dimensionahty before constructing ensembles. Then constructed ensembles based on random projection combined with K means in LPP subspace. Finally,we discussed how to choose the dimensionality of LPP subspace. hhe experiments show that ensembles generated by new algorithms perform better than those by Princi- pal Component Analysis with subsampling(PCASS) and simple Random Projection(RP) that was proposed before.

Key words: Cluster cnsembles,Dimension reduction,I_ocality preserving projections,Random projection