K-Means Fast Learning Artificial Neural Network, an alternative network for classification

Abstract

The K-Means Fast Learning Artificial Neural Network (K-FLANN) is an improvement of the original FLANN II (Tay and Evans, 1994). While FLANN II develops inconsistencies in clustering, influenced by data arrangements, K-FLANN bolsters this issue, through relocation of the clustered centroids. Results of the investigation are presented along with a discussion of the fundamental behavior of K-FLANN. Comparisons are made with the K-Means Clustering algorithm and the Kohonen SOM. A further discussion is provided on how K-FLANN can qualify as an alternative method for fast classification.