Affiliations: Mathematics and Computing Science, Saint Maryś University, Halifax, Canada
Correspondence:
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Corresponding author: Pawan Lingras, Mathematics and Computing Science, Saint Maryś University, Halifax, Canada. E-mail:pawan@cs.smu.ca
Abstract: Clustering groups objects based on their similarity
using unsupervised learning.
Clustering is an NP hard problem.
A number of clustering algorithms use heuristics to
create a reasonable grouping of objects.
However, clustering schemes created by different
heuristic algorithms do not always completely agree with each other.
For example, an object may belong to different clusters for different algorithms.
Therefore, researchers have proposed a number of clustering
ensemble techniques to combine the clustering schemes from
different algorithms.
This paper proposes a Rough Set based ensemble method
for preserving the inherent order in clustering.
The proposal is demonstrated with the help of daily price patterns
of commodities, which are grouped based on Black Scholes volatility
index as well as the distribution of prices.
