Abstract
The last decade has seen a growing body of research illustrating the advantages of the evolutionary multiobjective approach to data clustering. The scalability of such an approach, however, is a topic which merits more attention given the unprecedented volumes of data generated nowadays. This paper proposes a reduced-length representation for evolutionary multiobjective clustering. The new encoding explicitly prunes the solution space and allows the search method to focus on its most promising regions. Moreover, it allows us to precompute information in order to alleviate the computational overhead caused by the processing of candidate individuals during optimisation. We investigate the suitability of this proposal in the context of a representative algorithm from the literature: MOCK. Our results indicate that the new reduced-length representation significantly improves the effectiveness and computational efficiency of MOCK specifically, and can be seen as a further step towards a better scalability of evolutionary multiobjective clustering in general.
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Notes
- 1.
Whereas the clustering phase is responsible for generating a PFA comprising high-quality partitions, the model-selection phase is concerned with selecting and reporting one (or more) candidate partition(s) from this PFA as the problem’s solution.
- 2.
It should be noted that changing the optimisation criteria is the only adaptation of MOCK required by the representation scheme proposed in this paper. Such an adaptation, however, is only intended to exploit the advantages that the new representation can provide in terms of computational efficiency; this change is not found to affect MOCK’s behaviour and performance as discussed at the end of Sect. 3.2.
- 3.
Notice that when parameter \(\delta \) is set to \({\delta =0}\), the encoding scheme proposed here is equivalent to the original (full-length) locus-based adjacency representation.
- 4.
Since the creation of new solutions relies mainly on recombination (due to the low mutation rates used), the genetic material introduced during initialisation plays a key role in delimiting the extent of the solution space that is reached by the method.
- 5.
During the evolutionary process, the clusters defined by the partial solution are combined, which can only lead to the decrease of k and the increase of VAR.
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Garza-Fabre, M., Handl, J., Knowles, J. (2017). A New Reduced-Length Genetic Representation for Evolutionary Multiobjective Clustering. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham. https://doi.org/10.1007/978-3-319-54157-0_17
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