Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Extreme physical information and objective function in fuzzy clustering
Fuzzy Sets and Systems - Clustering and modeling
TOSCANA - a Graphical Tool for Analyzing and Exploring Data
GD '94 Proceedings of the DIMACS International Workshop on Graph Drawing
Formal concept analysis in information science
Annual Review of Information Science and Technology
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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.