Abstract
In this article, a novel consensus clustering method (voting-XCSc) via learning classifier system is proposed, which aims (1) to automatically determine the clustering number and (2) to achieve consensus results by reducing the influence coming from the randomness. When conducting the clustering for the data points, the proposed voting-XCSc will first employ the XCSc to generate a set of clustering results with different clustering numbers, and then it will adopt the dissociation-based strategy to experimentally determine the clustering number among all the candidates. Finally, a majority voting-based consensus method is applied to obtain the final clustering results. The proposed voting-XCSc has been evaluated on both the toy examples as well as two real clustering-related applications. i.e, lung cancer image identification, image segmentation. The results demonstrate the voting-XCSc can obtain the superior performance compared with XCSc, K-means, and other state-of-the-arts.
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Qian, L., Shi, Y., Gao, Y., Yin, H. (2013). Voting-XCSc: A Consensus Clustering Method via Learning Classifier System. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_73
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DOI: https://doi.org/10.1007/978-3-642-41278-3_73
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