GPU Strategies for Distance-Based Outlier Detection

The process of discovering interesting patterns in large, possibly huge, data sets is referred to as data mining, and can be performed in several flavours, known as "data mining functions." Among these functions, outlier detection discovers observations which deviate substantially from the...

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Veröffentlicht in:IEEE transactions on parallel and distributed systems Jg. 27; H. 11; S. 3256 - 3268
Hauptverfasser: Angiulli, Fabrizio, Basta, Stefano, Lodi, Stefano, Sartori, Claudio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithm design and analysis
Algorithms
Complexity theory
Computer architecture
Data mining
Distance-based outliers
Graphics processing units
Indexes
outlier detection
parallel and distributed algorithms
ISSN:1045-9219, 1558-2183
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Zusammenfassung:The process of discovering interesting patterns in large, possibly huge, data sets is referred to as data mining, and can be performed in several flavours, known as "data mining functions." Among these functions, outlier detection discovers observations which deviate substantially from the rest of the data, and has many important practical applications. Outlier detection in very large data sets is however computationally very demanding and currently requires high-performance computing facilities. We propose a family of parallel and distributed algorithms for graphic processing units (GPU) derived from two distance-based outlier detection algorithms: BruteForce and SolvingSet. The algorithms differ in the way they exploit the architecture and memory hierarchy of the GPU and guarantee significant improvements with respect to the CPU versions, both in terms of scalability and exploitation of parallelism. We provide a detailed discussion of their computational properties and measure performances with an extensive experimentation, comparing the several implementations and showing significant speedups.
Bibliographie:ObjectType-Article-1
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2016.2528984