Geometric visualization of clusters obtained from fuzzy clustering algorithms |
| |
Authors: | Luis Rueda Yuanquan Zhang |
| |
Affiliation: | a Department of Computer Science, University of Concepcion, Edmundo Larenas 215, Concepcion, Chile b School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, Ont., Canada N9B 3P4 |
| |
Abstract: | 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. |
| |
Keywords: | Fuzzy clustering Fuzzy _method=retrieve& _eid=1-s2 0-S0031320306000446& _mathId=si54 gif& _pii=S0031320306000446& _issn=00313203& _acct=C000051805& _version=1& _userid=1154080& md5=35ff4a1bb9be17e7b013bc247c4340cd')" style="cursor:pointer α-means" target="_blank">" alt="Click to view the MathML source" title="Click to view the MathML source">α-means Cluster visualization Expectation maximization |
本文献已被 ScienceDirect 等数据库收录! |
|