Paper Info
Title
Evolving principal component clustering with a low run-time complexity for LRF data mapping
Abstract
Graphical abstractDisplay Omitted HighlightsA novel approach for data stream clustering to linear model prototypes.Good performance, robust operation, low computational complexity and simple implementation.Validation of results by comparison to well-known algorithms. In this paper a new approach called evolving principal component clustering is applied to a data stream. Regions of the data described by linear models are identified. The method recursively estimates the data variance and the linear model parameters for each cluster of data. It enables good performance, robust operation, low computational complexity and simple implementation on embedded computers. The proposed approach is demonstrated on real and simulated examples from laser-range-finder data measurements. The performance, complexity and robustness are validated through a comparison with the popular split-and-merge algorithm.
Year
DOI
Venue
2015
10.1016/j.asoc.2015.06.044
Applied Soft Computing
Keywords
Field
DocType
Line extraction,Evolving clustering,Laser range finder
Data mining,Clustering high-dimensional data,CURE data clustering algorithm,Data stream clustering,Data mapping,Computer science,Robustness (computer science),Artificial intelligence,Time complexity,Cluster analysis,Machine learning,Computational complexity theory
Journal
Volume
Issue
ISSN
35
C
1568-4946
Citations 
PageRank 
References 
10
0.66
22
Authors
2
Name
Order
Citations
PageRank
Gregor Klancar1346.65
Igor Skrjanc235452.47