ISODATA classification with parameters estimated by evolutionary approach

Abstract

The lsodata algorithm is an unsupervised data classification algorithm. Its result depends strongly on two parameters: distance threshold for the union of clusters and threshold of typical deviation for the division of a cluster. A bad choice of these two parameters leads the algorithm to spiral out of control leaving the end only one class. To determine these parameters and improvements to this algorithm, evolution strategies are used. An evolutionary algorithm is adapted to estimate the two optimal thresholds to be used by the algorithm then lsodata. This approach is validated on simulation examples. The experimental results confirm the favorable convergence speed and good performance of the proposed algorithm.