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- Title
rFILTA: relevant and nonredundant view discovery from collections of clusterings via filtering and ranking.
- Authors
Lei, Yang; Vinh, Nguyen; Chan, Jeffrey; Bailey, James
- Abstract
Meta-clustering is a popular approach for finding multiple clusterings in the dataset, taking a large number of base clusterings as input for further user navigation and refinement. However, the effectiveness of meta-clustering is highly dependent on the distribution of the base clusterings and open challenges exist with regard to its stability and noise tolerance. In addition, the clustering views returned may not all be relevant, hence there is open challenge on how to rank those clustering views. In this paper we propose a simple and effective filtering algorithm that can be flexibly used in conjunction with any meta-clustering method. In addition, we propose an unsupervised method to rank the returned clustering views. We evaluate the framework (rFILTA) on both synthetic and real-world datasets, and see how its use can enhance the clustering view discovery for complex scenarios.
- Subjects
INFORMATION storage &; retrieval systems; COMPUTER systems; CLUSTER analysis (Statistics); INFORMATION filtering; SOFTWARE frameworks
- Publication
Knowledge & Information Systems, 2017, Vol 52, Issue 1, p179
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-016-1008-y