The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Distance-Based Outlier Detection on Uncertain Data
CIT '09 Proceedings of the 2009 Ninth IEEE International Conference on Computer and Information Technology - Volume 02
Distance-based outlier detection: consolidation and renewed bearing
Proceedings of the VLDB Endowment
Placer: semantic place labels from diary data
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Fast top-k distance-based outlier detection on uncertain data
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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Managing and mining uncertain data is becoming important with the increase in the use of devices responsible for generating uncertain data, for example sensors, RFIDs, etc. In this paper, we extend the notion of distance-based outliers for uncertain data. To the best of our knowledge, this is the first work on distance-based outlier detection on uncertain data of Gaussian distribution. Since the distance function for Gaussian distributed objects is very costly to compute, we propose a cell-based approach to accelerate the computation. Experimental evaluations of both synthetic and real data demonstrate effectiveness of our proposed approach.