文章基本信息

标题：Affine Arithmetic Self Organizing Map
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作者：Tony Bazzi ; Jasser Jasser ; Mohamed Zohdy 等
期刊名称：International Journal of Computer and Information Technology
印刷版ISSN：2279-0764
出版年度：2016
卷号：5
期号：4
页码：359-365
出版社：International Journal of Computer and Information Technology
摘要：This paper presents an arithmetic affine robust method to improve the performance of the self-organizing feature map which further preserves the similarities between data inputs and the weights matrix. The method presented herein targets the pre-processing and validation steps in the iterative process by filtering sensory uncertainties ensuing in data inaccuracy and large standard deviation affecting cluster affinity. The method introduces tolerances on incoming inputs to mitigate insignificant clustering creating computational burden and biasing the end result embedded in the topological map. The new technique utilizes mathematical means to modify both the competitive and adaptive stages of the conventional self-organizing map. To test the new algorithm, a simulation study was conducted to cluster Fisher's Iris dataset to improve the performance and robustness of the resulting map.
关键词：Self;Organizing Feature Maps; Affine SOM; Neural Networks; Unsupervised Learning; Robust Map; Inputs and Weights Error Mitigation;w