Electric load forecasting (ELF) is always employed to perform power systems management.
However, it is difficult to predict electric load due to the following issues: 1) electric load …

Multiple kernel clustering (MKC) has garnered considerable attention in recent years, aiming
to obtain an optimal partition from multiple base kernels. Existing MKC methods typically …

Nowadays, data in the real world often comes from multiple sources, but most existing multi-
view-Means perform poorly on linearly non-separable data and require initializing the …

Although K-Means clustering has been widely studied due to its simplicity, these methods
still have the following fatal drawbacks. Firstly, they need to initialize the cluster centers …

Multiple kernel clustering (MKC) aims to determine the optimal kernel from several pre-
computed basic kernels. Most of MKC algorithms follow a common assumption that the …

Although multi-view clustering (MVC) has achieved remarkable performance by integrating
the complementary information of views, it is inefficient when facing scalable data …

Dear Editor, This letter proposes a parameter-free multiple kernel clustering (MKC) method
by using shifted Laplacian reconstruction. Traditional MKC can effectively cluster nonlinear …

Clustering is a fundamental task in the machine learning and data mining community.
Among existing clustering methods, multiple kernel clustering (MKC) has been widely …

Many methods of multi-kernel clustering have a bias to power base kernels by ignoring other
kernels. To address this issue, in this paper, we propose a new method of multi-kernel graph …

With its simplicity and effectiveness, k-means is immensely popular, but it cannot perform
well on complex nonlinear datasets. Multiple kernel k-means (MKKM) demonstrates the …