多代表点自约束的模糊迁移聚类

以往建立在模糊C均值(fuzzyC-means, FCM)框架下利用源域虚拟簇中心作为迁移知识的迁移聚类算法容易受到离群点和噪声的干扰,且单个簇中心不足以描述簇结构。针对此问题,提出多代表点自约束的模糊迁移聚类算法,该算法引入样本代表权重机制为簇中每个样本分配代表权重来刻画簇结构,这种机制能更好的刻画簇结构,对离群点和噪声有较好的抑制作用;同时利用源域样本,重构目标域簇结构,并以此作为迁移知识进行目标域样本聚类,相对于利用单中心作为迁移知识来说,整体重构后的目标域簇结构所包含的迁移知识量更为丰富。试验结果表明。在人工数据集和真实数据集上,所提出的聚类算法相比对比算法, NMI和ARI最高提升了0.674 5和0.608 4。说明在迁移环境下,以代表点自约束作为知识迁移规则,所提出的聚类算法具有一定的聚类效果。

Transfer fuzzy clustering based on self-constraint of multiple medoids

Transfer clustering approaches derived from the fuzzy C-means (FCM) framework, which considered virtual centers from source domains as transfer knowledge, inherited the shortcomings of FCM. These methods were not robust to outliers and noises, and whose single cluster centers were not sufficient enough to capture the inner structures of clusters. To solve the problems, a transfer fuzzy clustering approach was proposed based on the self-constraint of multiple medoids. Prototype weights were introduced and assigned to each object to capture the inner structures of clusters. Such a weighting strategy could capture the inner structures of clusters more sufficiently and made the clustering more robust to outliers and noises; Furthermore, with the distribution of data in the source domain, the inner structure of data in the target domain was reconstructed, and the corresponding new structure was considered as the transfer knowledge to guide the clustering of the target domain. Relative to the use of single virtual center of each cluster as transfer knowledge, the updated inner structures of data in the target domain contained more knowledge. Experimental results demonstrated that the proposed approach achieved 0.674 5 and 0.608 4 improvements in terms of NMI and ARI on synthetic datasets and real-life datasets compared with introduced benchmarking approaches. Therefore, based on the transfer principle of the self-constraint of multiple medoids, the proposed clustering approach performed well in the transfer environment.