A principal components analysis neural gas algorithm for anomalies clustering

作者：Fang Xiufen^*; Liu Guisong; Huang Ting Zhu

来源：WSEAS Transactions on Systems, 2010, 9(1): 42-51.

• Clustering analysis
• Clustering methods
• Competitive neural network
• Convergent speed
• Data sets
• Gaussians
• K-means
• Neural gas
• Neural gas algorithms
• Neural gas network
• Neural gas networks
• Online learning algorithms
• Principal components analysis
• Soft competitive learning

Neural gas network is a single-layered soft competitive neural network, which can be applied to clustering analysis with fast convergent speed comparing to Self-organizing Map (SOM), K-means etc. Combining neural gas with principal component analysis, this paper proposes a new clustering method, namely principal components analysis neural gas (PCA-NG), and the online learning algorithm is also given. The soft competitive learning of PCA-NG is based on local principal subspace, which characterizes the profile of a certain cluster. We utilize the PCA-NG to the domain of intrusion detection. Some experiments are carried out to illustrate the performance of the proposed approach by using a synthetic Gaussian-distributed dataset and the KDD CUP 1999 Intrusion Detection Evaluation dataset.

• 出版日期2010
• 单位电子科技大学