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Clustering with Multiviewpoint-Based Similarity Measure
June 2012 (vol. 24 no. 6)
pp. 988-1001
ASCII Text
x 
 
Chee Keong Chan, Lihui Chen, Duc Thang Nguyen, "Clustering with Multiviewpoint-Based Similarity Measure," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 6, pp. 988-1001, June, 2012.
 
 
BibTex
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@article{ 10.1109/TKDE.2011.86,
author = { Chee Keong Chan and Lihui Chen and Duc Thang Nguyen},
title = {Clustering with Multiviewpoint-Based Similarity Measure},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {24},
number = {6},
issn = {1041-4347},
year = {2012},
pages = {988-1001},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.86},
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 - Clustering with Multiviewpoint-Based Similarity Measure
IS - 6
SN - 1041-4347
SP988
EP1001
EPD - 988-1001
A1 - Chee Keong Chan,
A1 - Lihui Chen,
A1 - Duc Thang Nguyen,
PY - 2012
KW - pattern clustering
KW - document handling
KW - clustering algorithm
KW - multiviewpoint-based similarity measure
KW - data objects
KW - dissimilarity measure
KW - informative assessment
KW - document clustering
KW - Clustering algorithms
KW - Strontium
KW - Euclidean distance
KW - Current measurement
KW - Proposals
KW - Partitioning algorithms
KW - Algorithm design and analysis
KW - similarity measure.
KW - Document clustering
KW - text mining
VL - 24
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Chee Keong Chan, Div. of Inf. Eng., Nanyang Technol. Univ., Singapore, Singapore
Lihui Chen, Div. of Inf. Eng., Nanyang Technol. Univ., Singapore, Singapore
Duc Thang Nguyen, Div. of Inf. Eng., Nanyang Technol. Univ., Singapore, Singapore
All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multiviewpoint-based similarity measure and two related clustering methods. The major difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. We compare them with several well-known clustering algorithms that use other popular similarity measures on various document collections to verify the advantages of our proposal.

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Index Terms:
pattern clustering,document handling,clustering algorithm,multiviewpoint-based similarity measure,data objects,dissimilarity measure,informative assessment,document clustering,Clustering algorithms,Strontium,Euclidean distance,Current measurement,Proposals,Partitioning algorithms,Algorithm design and analysis,similarity measure.,Document clustering,text mining
Citation:
Chee Keong Chan, Lihui Chen, Duc Thang Nguyen, "Clustering with Multiviewpoint-Based Similarity Measure," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 6, pp. 988-1001, June 2012, doi:10.1109/TKDE.2011.86
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