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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 1/2017

28.11.2016 | Regular Paper

rFILTA: relevant and nonredundant view discovery from collections of clusterings via filtering and ranking

verfasst von: Yang Lei, Nguyen Xuan Vinh, Jeffrey Chan, James Bailey

Erschienen in: Knowledge and Information Systems | Ausgabe 1/2017

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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.
Vorheriger Artikel Identification of critical situations via Event Processing and Event Trust Analysis
Nächster Artikel Exploiting interactions of review text, hidden user communities and item groups, and time for collaborative filtering

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Fußnoten
1
Please refer to Sect. 9.5.1 for more details about the dataset and experiments.
 
2
The similarity between two clusterings are measured by adjusted mutual information (AMI), which will be introduced in Sect. 4.1.
 
3
We demonstrate this point in the experiments part, refer to Fig. 21.
 
4
The normalized version of mutual information for scaling mutual information to [0, 1].
 
5
Even though we are not sampling the clustering space uniformly, the size of the meta-cluster can be considered as one reasonable standard.
 
7
The code of feature extraction is available at https://​github.​com/​adikhosla/​feature-extraction.
 
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Metadaten
Titel
rFILTA: relevant and nonredundant view discovery from collections of clusterings via filtering and ranking
verfasst von
Yang Lei
Nguyen Xuan Vinh
Jeffrey Chan
James Bailey
Publikationsdatum
28.11.2016
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 1/2017
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-016-1008-y

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