2014 RepresentativeClusteringofUncer

• (Züfle et al., 2014) ⇒ Andreas Züfle, Tobias Emrich, Klaus Arthur Schmid, Nikos Mamoulis, Arthur Zimek, and Matthias Renz. (2014). “Representative Clustering of Uncertain Data.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623725

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Representative+Clustering+of+Uncertain+Data
• http://dl.acm.org/citation.cfm?id=2623330.2623725&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; clustering; possible worlds; uncertain data

Abstract

This paper targets the problem of computing meaningful clusterings from uncertain data sets. Existing methods for clustering uncertain data compute a single clustering without any indication of its quality and reliability; thus, decisions based on their results are questionable. In this paper, we describe a framework, based on possible-worlds semantics; when applied on an uncertain dataset, it computes a set of representative clusterings, each of which has a probabilistic guarantee not to exceed some maximum distance to the ground truth clustering, i.e., the clustering of the actual (but unknown) data. Our framework can be combined with any existing clustering algorithm and it is the first to provide quality guarantees about its result. In addition, our experimental evaluation shows that our representative clusterings have a much smaller deviation from the ground truth clustering than existing approaches, thus reducing the effect of uncertainty.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 RepresentativeClusteringofUncer	Matthias Renz Arthur Zimek Andreas Züfle Tobias Emrich Klaus Arthur Schmid Nikos Mamoulis			Representative Clustering of Uncertain Data				10.1145/2623330.2623725		2014