Dimensionality Reduction Techniques for Document Clustering- A Survey
Abstract— Dimensionality reduction technique is applied to get rid of the inessential terms like redundant and noisy terms in documents. In this paper a systematic study is conducted for seven dimensionality reduction methods such as Latent Semantic Indexing (LSI), Random Projection (RP), Principle Component Analysis (PCA) and CUR decomposition, Latent Dirichlet Allocation(LDA), Singular value decomposition (SVD). Linear Discriminant Analysis(LDA)
Index Terms— Document clustering, CUR decomposition, Latent Dirichlet Allocation, Latent Semantic Indexing, Principle Component Analysis, Random Projection, Singular Value Decomposition.
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International Journal for Trends in Technology & Engineering © 2015 IJTET JOURNAL