An efficient hyperellipsoidal clustering algorithm for resource-constrained environments

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.

► Efficient hyperellipsoidal clustering algorithm for resource-constrained environments.
► Automatic selection of the number of clusters.
► Low computational cost (O(N))(O(N)).
► Explicit cluster boundary detection.
► Embedded outlier detection.