Manifold learning is essential for analyzing high-dimensional data, but it suffers from high
time complexity. To address this, researchers proposed using anchors and constructing a …

Ensemble clustering (EC) gains more and more attention because it can improve the
effectiveness and robustness of single clustering methods. A popular ensemble approach is …

Abstract Concept Factorization (CF), as a novel paradigm of representation learning, has
demonstrated superior performance in multi-view clustering tasks. It overcomes limitations …

Ensemble clustering algorithms have made significant progress in recent years due to their
excellent performance. However, most of these algorithms face two challenges: one is to …

Existing locally weighted ensemble clustering algorithms strive to weight each cluster and
take into account the differences among all clusters, but they tend to ignore the basic cluster …

Target maneuver recognition is a prerequisite for air combat situation awareness, trajectory
prediction, threat assessment and maneuver decision. To get rid of the dependence of the …

Clustering analysis is a powerful tool for discovering potential knowledge in datasets.
However, numerous existing clustering algorithms suffer from heavy reliance on parameter …

Clustering ensembles can obtain more superior final results by combining multiple different
clustering results. The qualities of the points, clusters, and partitions play crucial roles in the …

Clustering ensemble integrates multiple base clustering results to improve the stability and
robustness of the single clustering method. It consists of two principal steps: a generation …

为了降低大规模数据谱聚类计算负担, 进一步提高聚类的准确性和鲁棒性,
提出了一种基于三阶张量的大规模数据谱聚类集成算法. 首先, 提出一种混合代表最近邻近似 …