摘要
针对模糊C-均值(FCM)算法的局限性,提出了一种具有两阶段的模糊FCM聚类改进算法。通过加入点密度函数加权系数和样本特征矢量权重对FCM聚类算法中的目标函数进行改造,进而给出迭代推导公式和算法描述。该算法克服了样本分布不均匀和样本特征矢量对分类贡献不均衡的情况,有效地提高了聚类精度。最后利用KDD CUP 99数据集进行实验,结果表明该算法具有良好的可靠性和可行性。
Concerning the limits of the Fuzzy C-Means （FCM） clustering algorithm, a new kind of improved algorithm with two stages was put forward. The objective function in FCM clustering algorithm was improved by taking into account the modulus of dot density function and the weight of eigenvector; furthermore, the iterative reasoning formula and the description of algorithm were presented. This algorithm solved the problems of the sample＇s unequal distribution and the eigenvector of the sample contributing unevenly to the classes, which improved the clustering precision effectively. Experiments on data sets KDD CUP 99 show that the algorithm has good reliability and feasibility.
出处
《计算机应用》
CSCD
北大核心
2009年第5期1336-1338,共3页
journal of Computer Applications
基金
国家自然科学基金资助项目(60773209)
关键词
入侵检测
模糊聚类
C-均值算法
intrusion detection
fuzzy clustering
C-means algorithm
