As a representative evidential clustering algorithm, evidential c-means (ECM) provides a
deeper insight into the data by allowing an object to belong not only to a single class, but …

Clustering ensemble (CE) takes multiple clustering solutions into consideration in order to
effectively improve the accuracy and robustness of the final result. To reduce redundancy as …

Gaussian mixture model (GMM) is a well-known model-based approach for data clustering.
However, when the data samples are insufficient, the classical GMM-based clustering …

Evidential clustering is a promising clustering framework using Dempster–Shafer belief
function theory to model uncertain data. However, evidential clustering needs to estimate …

Clustering is widely used in text analysis, natural language processing, image segmentation
and other data mining fields. However, traditional clustering algorithms, such as K-means …

Transfer Learning is utilized to the traditional clustering methods to enhance the clustering
effect in the target domain with insufficient data. These methods can obtain good …

Clustering is widely used in text analysis, natural language processing, image segmentation
and other data mining fields. ECM (evidential c-means) is a powerful clustering algorithm …

In this paper, a Transfer Fuzzy C-Means clustering algorithm based on Maximum Mean
Discrepancy (TFCM-MMD) is proposed. TFCM-MMD solves the problem that the transfer …

该文针对迁移聚类问题, 提出一种基于最大**均差异的迁移模糊C 均值(TFCM-MMD) 聚类算法.
TFCM-MMD 解决了迁移模糊C 均值聚类算法在源域与目标域数据分布差异大的情况下迁移 …

Reinforcement learning with reward sha** is a well established but often computationally
expensive approach to large multiagent systems. Agent partitioning can reduce this …