Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A competitive elliptical clustering algorithm
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
The new k-windows algorithm for improving the k-means clustering algorithm
Journal of Complexity
Iterative shrinking method for clustering problems
Pattern Recognition
A survey on clustering algorithms for wireless sensor networks
Computer Communications
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Elliptical anomalies in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
DENCLUE 2.0: fast clustering based on kernel density estimation
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Clustering has been widely used as a fundamental data mining tool for the automated analysis of complex datasets. There has been a growing need for the use of clustering algorithms in embedded systems with restricted computational capabilities, such as wireless sensor nodes, in order to support automated knowledge extraction from such systems. Although there has been considerable research on clustering algorithms, many of the proposed methods are computationally expensive. We propose a robust clustering algorithm with low computational complexity, suitable for computationally constrained environments. Our evaluation using both synthetic and real-life datasets demonstrates lower computational complexity and comparable accuracy of our approach compared to a range of existing methods.