关键词: 高斯噪声, 增量式聚类算法, 距离度量, 高维数据, FCPM算法

Abstract: In order to cluster high-dimensional noisy data, a new distance metric based incremental clustering algo-rithm called ANFCM(c+p) (anti-noise fuzzy (c+p) means clustering) is proposed. Because the traditional FCM (fuzzy C-means clustering) algorithm is sensitive to the initialization of the cluster center, the proposed clustering algorithm integrates the incremental mechanism of the single-pass FCM called SpFCM (single pass fuzzy C-means clustering) algorithm together with the initialization strategy of cluster centers adopted in FCPM (fuzzy (c+p) means clustering) algorithm. That is several data points which are nearest to the cluster centers of the previous data block are added to the next data block for clustering. So it can avoid the sensitivity of FCM to noise. In particular, the proposed clustering algorithm takes a new improved distance metric, and then adopts the modified constraint condition and objective function. Through the above improvements, the influence degree of known and unknown classes in the algorithm can be distinguished, and the interaction degree between classes can be strengthened. Experimental results show that the proposed algorithm has good clustering effect and robustness for high dimen-sional noisy data.

Key words: Gaussian noise, incremental clustering algorithm, distance measure, high dimensional data, fuzzy (c+p) means clustering (FCPM) algorithm