Self-organizing maps
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized barycentric coordinates on irregular polygons
Journal of Graphics Tools
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
The Automated Multidimensional Detective
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Geometric Framework to Visualize Fuzzy-clustered Data
SCCC '05 Proceedings of the XXV International Conference on The Chilean Computer Science Society
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Visualizing fuzzy points in parallel coordinates
IEEE Transactions on Fuzzy Systems
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Fuzzy-clustering methods, such as fuzzy k-means and expectation maximization, allow an object to be assigned to multiple clusters with different degrees of membership. However, the memberships that result from fuzzy-clustering algorithms are difficult to be analyzed and visualized. The memberships, usually converted to 0-1 values, are visualized using parallel coordinates or different color shades. In this paper, we propose a new approach to visualize fuzzy-clustered data. The scheme is based on a geometric visualization, and works by grouping the objects with similar cluster memberships towards the vertices of a hyper-tetrahedron. The proposed method shows clear advantages over the existing methods, demonstrating its capabilities for viewing and navigating inter-cluster relationships in a spatial manner.