Multi-view clustering meets heterogeneous data: A fusion regularized method

The accumulated gene expression and DNA methylation data provide a great opportunity to exploit the mechanisms of biological systems. Current algorithms for the integration of gene expression and methylation data are characterized for undesirable performance because they fail to address the latent relations in the heterogeneous data. To solve this problem, we propose a novel multi-view clustering with self-representation learning and low-rank tensor constraint (MCSL-LTC), where the gene expression and DNA methylation data are treated as complementary views, and MCSL-LTC obtains a consensus partitioning reflecting the structure and features of various views. Specifically, self-representation learning is employed to explore the low-dimensional subspace structures embedded in different views, where the tensor norm is adopted to smooth different views, therefore improving the quality of features. Experimental results demonstrate that the proposed approach outperforms state-of-the-art baselines in terms of accuracy on both the social and cancer data, provides an effective and efficient method for the integration of heterogeneous genomic data.

A Multi-view Kernel Clustering framework for Categorical sequences

In recent years, multi-view clustering (MVC) has become a promising approach for analyzing heterogeneous multi-source data. However, during the collection of multi-view data, factors such as environmental interference or sensor failure often lead to the loss of view sample data, resulting in incomplete multi-view clustering (IMVC). Graph contrastive IMVC has demonstrated promising performance as an effective solution, which typically utilizes in-graph instances as positive pairs and out-of-graph instances as negative pairs. However, the construction of positive and negative pairs in this paradigm inevitably leads to graph noise Correspondence (GNC). To this end, we propose a new IMVC framework, namely robust graph contrastive learning (RGCL). Specifically, RGCL first completes the missing data by using a multi-view consistency transfer relationship graph. Then, to mitigate the impact of false negative pairs from graph contrastive, we propose noise-robust graph contrastive learning to mine intra-view consistency accurately. Finally, we present cross-view graph-level alignment to fully exploit the complementary information across different views. Experimental results on the six multi-view datasets demonstrate that our RGCL exhibits superiority and effectiveness compared with 9 state-of-the-art IMVC methods. The source code is available at https://github.com/DYZ163/RGCL.git.

In conventional unsupervised multi-view clustering (MVC), learning of representations from heterogeneous multiview data and its subsequent clustering are often separately optimized. The disparate optimization would lead to suboptimal performance because multi-view representation learning is not goal-directed. In this paper, we unify unsupervised multi-view learning and deep clustering in a novel discriminative Self-supervised Deep Correlational Multi-view Clustering (SDC-MVC) network. A new unified loss function is proposed to incorporate consensus information into discriminative representations, in which, the former is learnt by maximizing the canonical correlation among multi-view representations projected by neural networks, and the later is achieved through using confident clustering assignments as supervision. Further, multi-view representations are harnessed by our proposed Deep Serial Feature-level (DSF) Fusion layer. Experiments on three public datasets demonstrated that our method outperforms six state-of-the-art correlation-based MVC algorithms in terms of three evaluation metrics.

Multi-view intact space clustering

Multi-view clustering (MVC) has garnered significant attention in recent years due to its ability to leverage shared information across heterogeneous data sources. However, most existing methods focus on improving clustering performance while often neglecting potential conflicts between views and the uncertainty in view distributions. To address this limitation, we propose a novel framework named dual-reliable contrastive fusion multi-view clustering (DRCFMVC). This framework organically integrates uncertainty and conflict within a multi-view contrastive clustering paradigm for the first time. Through an innovative dual reliability weighting mechanism, uncertainty and conflict are systematically incorporated into contrastive learning. Specifically, high-dimensional features are first mapped to cluster distributions via a clustering network. Subsequently, Dempster–Shafer Theory (DST) estimates prediction uncertainty within each view, while Jensen–Shannon (JS) divergence quantifies conflict levels between views. These two complementary types of information are further integrated through the proposed dual-reliable weighting (DRW) fusion strategy, effectively guiding the contrastive optimization process to learn more stable and discriminative representations. Extensive experiments on eleven public benchmark datasets demonstrate that the proposed method achieves superior performance across multiple clustering evaluation metrics compared to existing state-of-the-art approaches. The source code can be made accessible at https://github.com/li-zi-qi/DRCFMVC.

Electronic health records (EHRs) involve heterogeneous data types such as binary, numeric and categorical attributes. As traditional clustering approaches require the definition of a single proximity measure, different data types are typically transformed into a common format or amalgamated through a single distance function. Unfortunately, this early transformation step largely pre-determines the cluster analysis results and can cause information loss, as the relative importance of different attributes is not considered. This exploratory work aims to avoid this premature integration of attribute types prior to cluster analysis through a multi-objective evolutionary algorithm called MVMC. This approach allows multiple data types to be integrated into the clustering process, explore trade-offs between them, and determine consensus clusters that are supported across these data views. We evaluate our approach in a case study focusing on systemic sclerosis (SSc), a highly heterogeneous auto-immune disease that can be considered a representative example of an EHRs data problem. Our results highlight the potential benefits of multi-view learning in an EHR context. Furthermore, this comprehensive classification integrating multiple and various data sources will help to understand better disease complications and treatment goals.KeywordsClusteringMulti-view clusteringSystemic sclerosisMulti-objective optimization