NDLI: Top-k Outlier Detection from Uncertain Data

Content Provider	Springer Nature Link
Author	Shaikh, Salman Ahmed Kitagawa, Hiroyuki
Copyright Year	2014
Abstract	Uncertain data are common due to the increasing usage of sensors, radio frequency identification (RFID), GPS and similar devices for data collection. The causes of uncertainty include limitations of measurements, inclusion of noise, inconsistent supply voltage and delay or loss of data in transfer. In order to manage, query or mine such data, data uncertainty needs to be considered. Hence, this paper studies the problem of top-k distance-based outlier detection from uncertain data objects. In this work, an uncertain object is modelled by a probability density function of a Gaussian distribution. The naive approach of distance-based outlier detection makes use of nested loop. This approach is very costly due to the expensive distance function between two uncertain objects. Therefore, a populated-cells list (PC-list) approach of outlier detection is proposed. Using the PC-list, the proposed top-k outlier detection algorithm needs to consider only a fraction of dataset objects and hence quickly identifies candidate objects for top-k outliers. Two approximate top-k outlier detection algorithms are presented to further increase the efficiency of the top-k outlier detection algorithm. An extensive empirical study on synthetic and real datasets is also presented to prove the accuracy, efficiency and scalability of the proposed algorithms.
Starting Page	128
Ending Page	142
Page Count	15
File Format	PDF
ISSN	14768186
Journal	International Journal of Automation and Computing
Volume Number	11
Issue Number	2
e-ISSN	17518520
Language	English
Publisher	Springer-Verlag
Publisher Date	2015-03-12
Publisher Place	Berlin, Heidelberg
Access Restriction	Subscribed
Subject Keyword	Top-k distance-based outlier detection uncertain data Gaussian uncertainty cell-based approach PC-list based approach Control, Robotics, Mechatronics Computer Applications Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Control and Systems Engineering Modeling and Simulation Computer Science Applications

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