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IDA
2009
Springer

How to Control Clustering Results? Flexible Clustering Aggregation

9 years 2 months ago
How to Control Clustering Results? Flexible Clustering Aggregation
One of the most important and challenging questions in the area of clustering is how to choose the best-fitting algorithm and parameterization to obtain an optiml clustering for the considered data. The clustering aggregation concept tries to bypass this problem by generating a set of separate, heterogeneous partitionings of the same data set, from which an aggregate clustering is derived. Up to now, almost every existing aggregation approach combines given crisp clusterings on the basis of pair-wise similarities. In this paper, we regard an input set of soft clusterings and show that it contains additional information that is efficiently useable for the aggregation. Our approach introduces an expanison of mentioned pair-wise similarities, allowing control and adjustment of the aggregation process and its result. Our exhaustive experiments show that our flexible approach offers adaptive results, improved identification of structures and high usability.
Martin Hahmann, Peter Benjamin Volk, Frank Rosenth
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where IDA
Authors Martin Hahmann, Peter Benjamin Volk, Frank Rosenthal, Dirk Habich, Wolfgang Lehner
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