Abstract
When dealing with multiple clustering solutions, the problem of extrapolating a small number of good different solutions becomes crucial. This problem is faced by the so called Meta Clustering [12], that produces clusters of clustering solutions. Often such groups, called meta-clusters, represent alternative ways of grouping the original data. The next step is to construct a clustering which represents a chosen meta-cluster. In this work, starting from a population of solutions, we build meta-clusters by hierarchical agglomerative approach with respect to an entropy-based similarity measure. The selection of the threshold value is controlled by the user through interactive visualizations. When the meta-cluster is selected, the representative clustering is constructed following two different consensus approaches. The process is illustrated through a synthetic dataset.
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Bifulco, I., Fedullo, C., Napolitano, F., Raiconi, G., Tagliaferri, R. (2008). Robust Clustering by Aggregation and Intersection Methods. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85567-5_91
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DOI: https://doi.org/10.1007/978-3-540-85567-5_91
Publisher Name: Springer, Berlin, Heidelberg
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