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AWST: A Novel Attribute Weight Selection Technique for Data Clustering
Rahman, M.A. and Islam, M.Z.
In this paper we propose a novel attribute weight selection technique called AWST that automatically determines
attribute weights for a clustering purpose. The main idea
of AWST is to assign weight on an attribute based on the
ability of the attribute to cluster the records of a dataset.
The attributes with higher abilities get higher weights for
clustering. We also propose a novel discretization
approach in AWST to discretize the domain values of a
numerical attribute. The performance of AWST is
compared with three other existing attribute weight
selection techniques. We compare the performance of
AWST with the three existing techniques namely SABC,
WKM and EB in terms of Silhouette Coefficient using
nine (9) natural datasets that we obtain from the UCI
machine learning repository. The experimental results
show that AWST outperforms than the existing techniques
on all datasets. The computational complexities and the
execution times of the techniques are also presented in the
paper. Note that, AWST requires less execution time than
many of the existing techniques used in this study. |
Cite as: Rahman, M.A. and Islam, M.Z. (2015). AWST: A Novel Attribute Weight Selection Technique for Data Clustering. In Proc. Thirteenth Australasian Data Mining Conference (AusDM 2015) Sydney, Australia. CRPIT, 168. Ong, K.L., Zhao, Y., Stone, M.G. and Islam, M.Z. Eds., ACS. 51-58 |
(from crpit.com)
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