Detecting global hyperparaboloid correlated clusters: a Hough-transform based multicore algorithm

Correlation clustering detects complex and intricate relationships in high-dimensional data by identifying groups of data points, each characterized by differents correlation among a (sub)set of features. Current correlation clustering methods generally limit themselves to linear correlations only....

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Vydáno v:Distributed and parallel databases : an international journal Ročník 37; číslo 1; s. 39 - 72
Hlavní autoři: Kazempour, Daniyal, Mauder, Markus, Kröger, Peer, Seidl, Thomas
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 15.03.2019
Springer Nature B.V
Témata:
Algorithms
Clustering
Computer Science
Correlation
Data points
Data Structures
Database Management
Hough transformation
Information Systems Applications (incl.Internet)
Memory Structures
Operating Systems
Parallel processing
Special Issue on Scientific and Statistical Data Management
Vibration
Multicore
Hough transform
Non-linear correlation clustering
Data mining
ISSN:0926-8782, 1573-7578
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Shrnutí:Correlation clustering detects complex and intricate relationships in high-dimensional data by identifying groups of data points, each characterized by differents correlation among a (sub)set of features. Current correlation clustering methods generally limit themselves to linear correlations only. In this paper, we introduce a method for detecting global non-linear correlated clusters focusing on quadratic relations. We introduce a novel Hough transform for the detection of hyperparaboloids and apply it to the detection of hyperparaboloid correlated clusters in arbitrary high-dimensional data spaces. We further provide a solution for utilizing all available CPU cores on a system. For this we simply split the Hough space among a pre-defined axis into a number of equi-sized partitions. In this paper we show that this most simple way of parallelization already improves the runtime significantly. Non-linear correlation clustering like our method can reveal valuable insights which are not covered by current linear versions. Our empirical results on synthetic and real world data reveal that the proposed method is robust against noise, jitter and irregular densities.
Bibliografie:ObjectType-Article-1
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ISSN:0926-8782
1573-7578
DOI:10.1007/s10619-018-7246-0