Constructing effective cluster ensembles based on Locally Linear Embedding

This paper studies how to construct cluster ensembles for high dimensional data. We examine a different approach to constructing cluster ensembles. To address high dimensionality, we focus on ensemble construction methods that build on a popular dimension reduction techniques, Locally Linear Embedding (LLE). Our ensemble constructor is based on random projection in LLE subspace. We present evidence showing that ensembles generated by new algorithms perform better than those by Principal Component Analysis with subsampling (PCASS) and Random Projection simply (RP) that proposed before. Experimental results demonstrate the effectiveness of the proposed methods on several real-world data sets.