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A Link-Based Cluster Ensemble Approach for Categorical Data Clustering
March 2012 (vol. 24 no. 3)
pp. 413-425
ASCII Text
x 
 
Natthakan Iam-On, Tossapon Boongoen, Simon Garrett, Chris Price, "A Link-Based Cluster Ensemble Approach for Categorical Data Clustering," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 3, pp. 413-425, March, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2010.268,
author = {Natthakan Iam-On and Tossapon Boongoen and Simon Garrett and Chris Price},
title = {A Link-Based Cluster Ensemble Approach for Categorical Data Clustering},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {24},
number = {3},
issn = {1041-4347},
year = {2012},
pages = {413-425},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.268},
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 - A Link-Based Cluster Ensemble Approach for Categorical Data Clustering
IS - 3
SN - 1041-4347
SP413
EP425
EPD - 413-425
A1 - Natthakan Iam-On,
A1 - Tossapon Boongoen,
A1 - Simon Garrett,
A1 - Chris Price,
PY - 2012
KW - Clustering
KW - categorical data
KW - cluster ensembles
KW - link-based similarity
KW - data mining.
VL - 24
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Natthakan Iam-On, Mae Fah Luang University, Muang
Tossapon Boongoen, Royal Thai Air Force Academy, Saimai
Simon Garrett, Aispire Consulting Ltd., Aberystwyth
Chris Price, Aberystwyth University, Aberystwyth
Although attempts have been made to solve the problem of clustering categorical data via cluster ensembles, with the results being competitive to conventional algorithms, it is observed that these techniques unfortunately generate a final data partition based on incomplete information. The underlying ensemble-information matrix presents only cluster-data point relations, with many entries being left unknown. The paper presents an analysis that suggests this problem degrades the quality of the clustering result, and it presents a new link-based approach, which improves the conventional matrix by discovering unknown entries through similarity between clusters in an ensemble. In particular, an efficient link-based algorithm is proposed for the underlying similarity assessment. Afterward, to obtain the final clustering result, a graph partitioning technique is applied to a weighted bipartite graph that is formulated from the refined matrix. Experimental results on multiple real data sets suggest that the proposed link-based method almost always outperforms both conventional clustering algorithms for categorical data and well-known cluster ensemble techniques.

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Index Terms:
Clustering, categorical data, cluster ensembles, link-based similarity, data mining.
Citation:
Natthakan Iam-On, Tossapon Boongoen, Simon Garrett, Chris Price, "A Link-Based Cluster Ensemble Approach for Categorical Data Clustering," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 3, pp. 413-425, March 2012, doi:10.1109/TKDE.2010.268
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