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Iterative Projected Clustering by Subspace Mining
February 2005 (vol. 17 no. 2)
pp. 176-189
ASCII Text
x 
 
Man Lung Yiu, Nikos Mamoulis, "Iterative Projected Clustering by Subspace Mining," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 2, pp. 176-189, February, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2005.29,
author = {Man Lung Yiu and Nikos Mamoulis},
title = {Iterative Projected Clustering by Subspace Mining},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {17},
number = {2},
issn = {1041-4347},
year = {2005},
pages = {176-189},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.29},
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 - Iterative Projected Clustering by Subspace Mining
IS - 2
SN - 1041-4347
SP176
EP189
EPD - 176-189
A1 - Man Lung Yiu,
A1 - Nikos Mamoulis,
PY - 2005
KW - Database management
KW - database applications
KW - clustering
KW - classification
KW - and association rules.
VL - 17
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Irrelevant attributes add noise to high-dimensional clusters and render traditional clustering techniques inappropriate. Recently, several algorithms that discover projected clusters and their associated subspaces have been proposed. In this paper, we realize the analogy between mining frequent itemsets and discovering dense projected clusters around random points. Based on this, we propose a technique that improves the efficiency of a projected clustering algorithm (DOC). Our method is an optimized adaptation of the frequent pattern tree growth method used for mining frequent itemsets. We propose several techniques that employ the branch and bound paradigm to efficiently discover the projected clusters. An experimental study with synthetic and real data demonstrates that our technique significantly improves on the accuracy and speed of previous techniques.

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Index Terms:
Database management, database applications, clustering, classification, and association rules.
Citation:
Man Lung Yiu, Nikos Mamoulis, "Iterative Projected Clustering by Subspace Mining," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 2, pp. 176-189, Feb. 2005, doi:10.1109/TKDE.2005.29
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