A Single-Layer Semi-Supervised Feed Forward Neural Network Clustering Method

Malaysian Journal of Computer Science

Roya Asadi¹  Mitra Asadi¹  Sameem A. Kareem¹  Shokoofeh Asadi¹ 

关键词: Artificial Neural Network;    Feed Forward Neural Network;    Supervised and Unsupervised learning;    Semi-supervised Clustering;    Real weight;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: University of Malaya * Faculty of Computer Science and Information Technology

【 摘 要 】

The aim of this research is to develop and propose a single-layer semi-supervised feed forward neural networkclustering method with one epoch training in order to solve the problems of low training speed, accuracy andhigh time and memory complexities of clustering. A code book of non-random weights is learned through theinput data directly. Then, the best match weight (BMW) vector is mined from the code book, and consequentlyan exclusive total threshold of each input data is calculated based on the BMW vector. The input data areclustered based on their exclusive total thresholds. Finally, the method assigns a class label to each input databy using a K-step activation function for comparing the total thresholds of the training set and the test set. Theclass label of other unlabeled and unknown input test data are predicted based on their clusters or trial anderror technique, and the number of clusters and density of each cluster are updated. In order to evaluate theresults , the proposed method was used to cluster five datasets, namely thebreast cancer Wisconsin, Iris, Spam,Arcene and Yeast from the University of California Irvin (UCI) repository and a breast cancer dataset from theUniversity of Malaya Medical center (UMMC), and their results were compared with the results of the severalrelated methods. The experimental results show the superiority of the proposed method.

【 授权许可】

Unknown