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Document Clustering Using Locality Preserving Indexing
December 2005 (vol. 17 no. 12)
pp. 1624-1637
ASCII Text
x 
 
Deng Cai, Xiaofei He, Jiawei Han, "Document Clustering Using Locality Preserving Indexing," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 12, pp. 1624-1637, December, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2005.198,
author = {Deng Cai and Xiaofei He and Jiawei Han},
title = {Document Clustering Using Locality Preserving Indexing},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {17},
number = {12},
issn = {1041-4347},
year = {2005},
pages = {1624-1637},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.198},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Document Clustering Using Locality Preserving Indexing
IS - 12
SN - 1041-4347
SP1624
EP1637
EPD - 1624-1637
A1 - Deng Cai,
A1 - Xiaofei He,
A1 - Jiawei Han,
PY - 2005
KW - Index Terms- Document clustering
KW - locality preserving indexing
KW - dimensionality reduction
KW - semantics.
VL - 17
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
We propose a novel document clustering method which aims to cluster the documents into different semantic classes. The document space is generally of high dimensionality and clustering in such a high dimensional space is often infeasible due to the curse of dimensionality. By using Locality Preserving Indexing (LPI), the documents can be projected into a lower-dimensional semantic space in which the documents related to the same semantics are close to each other. Different from previous document clustering methods based on Latent Semantic Indexing (LSI) or Nonnegative Matrix Factorization (NMF), our method tries to discover both the geometric and discriminating structures of the document space. Theoretical analysis of our method shows that LPI is an unsupervised approximation of the supervised Linear Discriminant Analysis (LDA) method, which gives the intuitive motivation of our method. Extensive experimental evaluations are performed on the Reuters-21578 and TDT2 data sets.

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Index Terms:
Index Terms- Document clustering, locality preserving indexing, dimensionality reduction, semantics.
Citation:
Deng Cai, Xiaofei He, Jiawei Han, "Document Clustering Using Locality Preserving Indexing," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 12, pp. 1624-1637, Dec. 2005, doi:10.1109/TKDE.2005.198
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