We present a graph-based algorithm for combining multiple clusterings which is based on the idea of maximally complete subgraphs-also known as cliques. Cliques ...
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Our contributions are a novel method for combining a collection of clusterings into a final clustering which is based on cliques, and a novel output-sensitive ...
Apr 25, 2024 · CLICOM: Cliques for combining multiple clusterings. Expert Syst ... Combining multiple clusterings using similarity graph. Pattern ...
Co-authors ; CLICOM: Cliques for combining multiple clusterings. S Mimaroglu, M Yagci. Expert Systems With Applications 39 (2), 1889-1901, 2012. 29, 2012.
Oct 22, 2024 · We explore the idea of evidence accumulation (EAC) for combining the results of multiple clusterings. First, a clustering ensemble--a set of object partitions, ...
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Consensus clustering refers to combining multiple clusterings over a common set of objects into a single consolidated partition. ... "CLICOM: Cliques for ...
Our method for arbitrary shape object detection is based on COMUSA which is an efficient algorithm for combining multiple clusterings. Extensive experimental ...
Nov 1, 2019 · This combines multiple clusterings of a set of objects into a single integrated clustering. Consensus clustering algorithms attempt to find ...
CLICOM: Cliques for combining multiple clusterings. S Mimaroglu, M Yagci. Expert Systems With Applications 39 (2), 1889-1901, 2012. 29, 2012. A ranker ensemble ...
Multiple clustering aims at discovering diverse ways of organizing data into clusters. Despite the progress made, it's still a challenge for users to ...