Cause I’m a Genial Imprecise Point: Outlier Detection for Uncertain Data

2021 ◽  
pp. 165-178
Author(s):  
Vahideh Keikha ◽  
Hamidreza Keikha ◽  
Ali Mohades
Keyword(s):  
Outlier Detection ◽  
Uncertain Data

Minimal Rare Pattern-Based Outlier Detection Approach For Uncertain Data Streams Under Monotonic Constraints

2021 ◽  
Author(s):  
Saihua Cai ◽  
Jinfu Chen ◽  
Haibo Chen ◽  
Chi Zhang ◽  
Qian Li ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
State Of The Art ◽  
Uncertain Data ◽  
Small Scale ◽  
Detection Accuracy ◽  
Detection Approach ◽  
The Matrix ◽  
Uncertain Data Streams

Abstract Existing association-based outlier detection approaches were proposed to seek for potential outliers from huge full set of uncertain data streams ($UDS$), but could not effectively process the small scale of $UDS$ that satisfies preset constraints; thus, they were time consuming. To solve this problem, this paper proposes a novel minimal rare pattern-based outlier detection approach, namely Constrained Minimal Rare Pattern-based Outlier Detection (CMRP-OD), to discover outliers from small sets of $UDS$ that satisfy the user-preset succinct or convertible monotonic constraints. First, two concepts of ‘maximal probability’ and ‘support cap’ are proposed to compress the scale of extensible patterns, and then the matrix is designed to store the information of each valid pattern to reduce the scanning times of $UDS$, thus decreasing the time consumption. Second, more factors that can influence the determination of outlier are considered in the design of deviation indices, thus increasing the detection accuracy. Extensive experiments show that compared with the state-of-the-art approaches, CMRP-OD approach has at least 10% improvement on detection accuracy, and its time cost is also almost reduced half.

Parallel outlier detection on uncertain data for GPUs

2014 ◽  
Vol 33 (3) ◽  
pp. 417-447 ◽  
Author(s):  
Takazumi Matsumoto ◽  
Edward Hung ◽  
Man Lung Yiu
Keyword(s):  
Outlier Detection ◽  
Uncertain Data
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