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

A. Phuan, S. Prakash
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引用次数: 14

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.
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K-Means快速学习人工神经网络,一种用于分类的替代网络
K-Means快速学习人工神经网络(K-FLANN)是对原始FLANN II的改进(Tay和Evans, 1994)。虽然FLANN II受到数据排列的影响,在聚类中产生不一致,但K-FLANN通过重新定位聚类质心来支持这一问题。本文给出了研究结果,并讨论了K-FLANN的基本行为。并与K-Means聚类算法和Kohonen SOM算法进行了比较。进一步讨论了K-FLANN如何成为快速分类的替代方法。
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