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An empirical study of cluster-based MOEA/D bare bones PSO for data clustering
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Abstract:
Previously, cluster-based multi or many objective function techniques were proposed to
reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the
objective space to solve engineering problems. In this work, we applied a cluster-based approach
for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare
bones particle swarm optimization for data clustering and investigated its clustering performance. In
our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here,
we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with
six multiobjective evolutionary clustering algorithms from the existing literature and ten from our
previous work. The proposed technique was found to perform well on datasets highly overlapping
clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for
clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based
MOEA found were used to solve other problems that are not data clustering problems. By clustering
Pareto solutions and evaluating new candidates against the found cluster representatives, local search
is introduced in the solution selection process within the objective space, which can be effective on
datasets with highly overlapping clusters. This is an added layer of search control in the objective
space. The results are found to be promising, prompting different areas of future research which are
discussed, including the study of its effects with an increasing number of clusters as well as with
other objective functions.
Description:
Date:
2021
Authors:
Lai, Daphne Teck Ching
Sato, Yuji
Publisher:
MDPI
