Title :
Constructing effective cluster ensembles based on Locally Linear Embedding
Author :
Huan, Lei ; Huang, Shan ; Zhou, Jingbo
Author_Institution :
Inst. of Command Autom., PLA Univ. of Sci. & Technol., Nanjing, China
Abstract :
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.
Keywords :
pattern clustering; principal component analysis; sampling methods; LLE subspace; cluster ensemble construction; dimension reduction technique; ensemble constructor; high dimensional data; locally linear embedding; principal component analysis; random projection; real-world data sets; subsampling; Algorithm design and analysis; Clustering algorithms; Machine learning; Pattern recognition; Principal component analysis; Spatial databases; cluster ensembles; dimension reduction; locally linear embedding;
Conference_Titel :
Computer Science and Service System (CSSS), 2011 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-9762-1
DOI :
10.1109/CSSS.2011.5974129