Traditionally, spectral clustering is limited to a single objective: finding the normalized min-cut of a single graph. However, many real-world datasets, such as scientific data (fMRI scans of different individuals), social data (different types of connections between people), web data (multi-type data), are generated from multiple heterogeneous sources. How to optimally combine knowledge from ...

Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intuitiveness and robustness to noise corruptions. However, it aggressively tries...

In data mining, clustering is the one of efficient research concept in real time analysis, evaluation attribute representation main issue artificial intelligence related areas. Multi labeled gives high amount valuable data, which describes and be trending multi categorical analysis. dimensional combined complementary from different dimensions to provide results various conditions. Different vie...

In the past decade, multi-view clustering has received a lot of attention due to popularity data. However, not all samples can be observed from every view some unavoidable factors, resulting in incomplete (IMC) problem. Up until now, most efforts for IMC problem have been made on learning consensus representations or graphs, while many missing views are ignored, making it impossible capture inf...

Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restricting their feasibility for large-scale datasets. Second, typically fuse information via one-stage fusion, neglecting possibilities in multi-stage fusions. Third, dataset-specific hyperparameter-tuning is frequently req...