Most existing multi-view clustering algorithms suffer from issues such as incomplete fusion mechanisms， insufficient exploration of multi-view collaborative relationships， and weak robustness. These limitations result in low consistency in clustering results and unstable performance under noise and redundant information. To address these issues， a Multi-View Clustering algorithm based on Bipartite Graph and Consensus graph learning （BGC-MVC） was developed to enhance clustering consistency and complementarity by integrating information from multiple views. Specifically， BGC-MVC constructed a bipartite graph to capture neighborhood relationships across different views， and then learned a consensus graph to strengthen inter-view similarity. It integrated embeddings of the original multi-view data into a unified framework that combined graph learning with clustering process， thereby improving the overall clustering performance. Experimental results demonstrate that BGC-MVC achieves significant improvements in accuracy， F-score， Normalized Mutual Information （NMI） and purity under convergence conditions. Notably， on the MSRC_v1 dataset， BGC-MVC outperforms Large-scale Multi-View Subspace Clustering （LMVSC） by increasing the F-score by 19.48 percentage points and exhibits enhanced robustness and accuracy.

Multi-view clustering task based on incomplete data has become one of the research hotspots in the field of unsupervised learning. However， most multi-view clustering algorithms based on “shallow” models often find it difficult to extract and characterize potential feature structures within views when dealing with large-scale high-dimensional data. At the same time， the stacking or averaging methods of multi-view information fusion ignore the differences between views and does not fully consider the different contributions of each view to building a common consensus representation. To address the above issues， an Incomplete Multi-View Clustering algorithm based on Self-Attention Fusion （IMVCSAF） was proposed. Firstly， the potential features of each view were extracted on the basis of a deep autoencoder， and the consistency information among views was maximized by using contrastive learning. Secondly， a self-attention mechanism was adopted to recode and fuse the potential representations of each view， and the inherent causality as well as feature complementarity between different views was considered and mined comprehensively. Thirdly， based on the common consensus representation， the potential representation of missing instance was predicted and recovered， thereby fully implementing the process of multi-view clustering. Experimental results on Scene-15， LandUse-21， Caltech101-20 and Noisy-MNIST datasets show that， the accuracy of IMVCSAF is higher than those of other comparison algorithms while meeting the convergence requirements. On Noisy-MNIST dataset with 50% miss rate， the accuracy of IMVCSAF is 6.58 percentage points higher than that of the second best algorithm — COMPETER （inCOMPlete muLti-view clustEring via conTrastivE pRediction）.

The density clustering algorithm has been widely used because of its robustness to noise and the ability to find clusters of any shapes. However， in practical applications， this type of algorithms faces the problem of poor clustering effect due to the uneven distribution of the densities of different clusters in the dataset and the difficulty of distinguishing the borders between clusters. In order to solve the above problem， a Varied Density Clustering algorithm based on Border point Detection （VDCBD） was proposed. Firstly， the border points between varied density clusters were recognized based on the given relative density measurement method to enhance the separability of adjacent clusters. Secondly， the points in the non-border area were clustered to find the core class structures of the dataset. Secondly， the detected border points were allocated to the corresponding core class structures according to the principle of high-density neighbor allocation. Finally， the noise points in the dataset were recognized based on the class structure information. The proposed algorithm was compared and analyzed with the clustering algorithms such as K-means， Density-Based Spatial Clustering of Applications with Noise （DBSCAN）algorithm， Density Peaks Clustering Algorithm （DPCA）， CLUstering based on Backbone （CLUB）algorithm， Border Peeling clustering （BP）algorithm on artificial datasets and UCI datasets. Experimental results show that the proposed algorithm can effectively solve the problems of uneven distribution of density and indistinguishable borders， and is superior to the existing algorithms on the evaluation indicators of Adjusted Rand Index （ARI）， Normalized Mutual Information （NMI）， F-Measure （FM）， and Accuracy （ACC）； in the analysis of operating efficiency， when the data size is relatively large， the operating efficiency of VDCBD is higher than those of DPCA， CLUB and BP algorithms.