Geometric visualization of clusters obtained from fuzzy clustering algorithms

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,...

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Veröffentlicht in:Pattern recognition Jg. 39; H. 8; S. 1415 - 1429
Hauptverfasser: Rueda, Luis, Zhang, Yuanquan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.08.2006
Schlagworte:
Cluster visualization
Expectation maximization
Fuzzy [formula omitted]-means
Fuzzy clustering
Expectation maximization
Fuzzy clustering
Fuzzy α -means
Cluster visualization
ISSN:0031-3203, 1873-5142
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Zusammenfassung: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.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2006.02.006