General kernel clustering only concern relationship within clusters while ignoring the issue between clusters. Misclassification easily occurs when clustering data sets with fuzzy and noisy boundaries. To solve this problem, a new clustering algorithm was proposed based on Kernel Fuzzy C-Means (KFCM) clustering algorithm, which was called Kernel Fuzzy C-Means with Maximum distance between clusters (MKFCM). Considering the relationship between within-cluster elements and between-cluster elements, a penalty term representing the distance between centers in feature space and a control parameter were introduced. In this way, the distance between clustering centers was broadened and the samples near boundaries were better classified. Compared with traditional clustering algorithms, the experiments results on simulated data sets show that the proposed algorithm reduces the offset distance of clustering centers obviously. On man-made Gaussian data sets, the ACCuracy (ACC), Normalized Mutual Information (NMI) and Rand Index (RI) of the proposed algorithm were improved to 0.9132, 0.7575 and 0.9138. The proposed algorithm shows its theoretical research significance on data sets with fuzzy and noisy boundaries.

The classificationn decision process of Support Vector Machine (SVM) involves the study of original training samples, which easily causes privacy disclosure. To solve this problem, a classification approach with privacy preservation called IC-SVM (Information Concentration Support Vector Machine) was proposed based on information concentration. Firstly, the original training data was concentrated using Fuzzy C-Means (FCM) clustering algorithm according to each sample point and its neighbors. Then clustering centers were reconstructed to get new samples through information concentration. Finally, the new samples were trained to get decision function, by which classification was done. The experimental results on UCI and PIE show that the proposed method achieves good classification accuracy as well as preventing privacy disclosure.