基于知识利用的迁移学习一般化增强模糊划分聚类算法

针对非充分数据集及噪声对聚类分析的干扰，基于模糊C均值(FCM)框架下的聚类技术，即一般化的增强模糊划分聚类算法(GIFP-FCM)，探讨具有迁移学习能力的聚类方法--融入迁移学习机制的GIFP-FCM算法(T-GIFP-FCM)。该算法通过有效利用历史相关场景(域)总结得到的知识来指导当前场景(域)中信息不足时的聚类任务，从而提高聚类效果。通过在模拟数据集及真实数据集上的仿真实验，结果显示文中算法较之传统算法在处理信息不足任务时具有更佳的性能。

Transfer Generalized Fuzzy C-Means Clustering Algorithm with Improved Fuzzy Partitions by Leveraging Knowledge

To weaken the influence of the insufficient datasets and noises on the clustering analysis, a clustering algorithm, transfer generalized fuzzy C-means with improved fuzzy partitions (T-GIFP-FCM) is proposed based on the FCM framework-based clustering algorithm GIFP-FCM. By leveraging the historical knowledge in the related scene (domain) , the performance of T-GIFP-FCM is enhanced. Even if the data in the current scene are not enough, the promising clustering results can be obtained. The experimental results show the proposed algorithm has better performance compared with the traditional algorithms in situations of insufficient data.