Abstract
Because clustering is an unsupervised procedure, clustering results need be judged by external criteria called validity indices. These indices play an important role in determining the number of clusters in a given dataset. A general approach for determining this number is to select the optimal value of a certain cluster validity index. Most existing indices give good results for data sets with well separated clusters, but usually fail for complex data sets, for example, data sets with overlapping clusters. In this paper, we propose a new approach for clustering quality evaluation while combining fuzzy logic with Formal Concept Analysis based on concept lattice. We define a formal quality index including the separation degree and the overlapping rate.
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Sassi, M., Touzi, A.G., Ounelli, H. (2007). Clustering Quality Evaluation Based on Fuzzy FCA. In: Wagner, R., Revell, N., Pernul, G. (eds) Database and Expert Systems Applications. DEXA 2007. Lecture Notes in Computer Science, vol 4653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74469-6_62
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DOI: https://doi.org/10.1007/978-3-540-74469-6_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74467-2
Online ISBN: 978-3-540-74469-6
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