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 step, which is about the creation of base clusterings, and a consensus function, which is the integration of all clusterings obtained in the generation step. However, most of the existing base clustering algorithms used in the generation step are shallow clustering algorithms such as k-means. These shallow clustering algorithms do not work well or even fail when dealing with large-scale, high-dimensional unstructured data. The emergence of deep clustering algorithms provides a solution to address this challenge. Deep clustering combines the unsupervised commonality of deep representation learning to address complex high-dimensional data clustering, which has achieved excellent performance in many fields. In light of this, we introduce deep clustering into clustering ensemble and propose an improved selective deep-learning-based clustering ensemble algorithm (ISDCE). ISDCE exploits the deep clustering algorithm with different initialization parameters to generate multiple diverse base clusterings. Next, ISDCE constructs ensemble quality and diversity evaluation metrics of base clusterings to select higher-quality and rich-diversity candidate base clusterings. Finally, a weighted graph partition consensus function is utilized to aggregate the candidate base clusterings to obtain a consensus clustering result. Extensive experimental results on various types of datasets demonstrate that ISDCE performs significantly better than existing clustering ensemble approaches.

Clustering ensemble, which aims to learn a robust consensus clustering from multiple weak base clusterings, has achieved promising performance on various applications. With the development of big data, the scale and complexity of data is constantly increasing. However, most existing clustering ensemble methods typically employ shallow clustering algorithms to generate base clusterings. When confronted with high-dimensional complex data, these shallow algorithms fail to fully utilize the intricate features present in the latent data space. As a result, the quality and diversity of the generated base clusterings are insufficient, thus affecting the subsequent ensemble performance. To address this issue, we propose a novel clustering ensemble algorithm for handling deep embeddings using cluster confidence (CEDECC) to improve the robustness and performance. Instead of simply combining deep clustering with clustering ensembles, we take into consideration that the performance of existing deep clustering methods heavily relies on the quality of low-dimensional embeddings generated during the pre-training stage. The quality of embeddings is unstable due to the influence of different initialization parameters. In CEDECC, specifically, we first construct a cluster confidence measure to evaluate the quality of low-dimensional embeddings. Typically, high-quality low-dimensional embeddings yield accurate clustering results with the same model parameters. Then, we utilize multiple high-quality embeddings to generate the base partitions. In the ensemble strategy phase, we consider the cluster-wise diversity and propose a novel ensemble cluster estimation to improve the overall consensus performance of the model. Extensive experiments on three benchmark datasets and four real-world biological datasets have demonstrated that the proposed CEDECC consistently outperforms the state-of-the-art clustering ensemble methods.

With the development of big data, cloud computing, artificial intelligence, Internet of Things, blockchain and other technologies, digitalization is directly driving the transformation of socio-economic development. The digital transformation of higher education, as a key area for achieving sustainable development, has become an important trend in future educational reform and development. Digital transformation of education is a systematic change of technology-enabled education, which is a continuous evolutionary process with strong complexity and dynamics. The establishment of an education digitalization framework or maturity model is an important groundwork for digital transformation of education. It will provide a clear strategy and path for the digital transformation of education and the construction of "digital campus", and promote the sustainability of the practice. Therefore, this paper explores the technology of data collection platform construction in the digital transformation of higher education based on light applications and APPs, so as to provide more references for further enriching the education digitalization framework and related models.

The goal of multi-view graph clustering (MVGC) for remote sensing data is to obtain a consistent partitioning by capturing complementary and consensus information across multiple views. However, numerous ambiguous background samples in multi-view remote sensing data increase structural heterogeneity while simultaneously hindering effective information extraction and fusion. Existing MVGC methods cannot selectively integrate and fully refine both graph structure and node attribute information for consensus representation learning. Furthermore, current methods tend to overlook distant nodes, thus failing to capture the global graph structure. To solve these issues, we propose a novel method called Improved Multi-View Graph Clustering with Global Graph Refinement (IMGCGGR). Specifically, we first design a view-specific fusion network (VSFN) to extract and integrate node attribute and structural information into view-specific representation for each view. VSFN not only utilizes a global self-attention mechanism to enhance the global properties of structural information but also constructs a clustering loss through a self-supervised strategy to guide the view-specific clustering distribution assignment. Moreover, to enhance the capability of view-specific representation, a learnable attention-driven aggregation strategy is introduced to flexibly fuse the attribute and structural feature. Then, we adopt a cross-view fusion module to adaptively merge multiple view-specific representations for generating the final consensus representation. Comprehensive experiments show that IMGCGGR achieves significant clustering performance improvements over baseline methods across various benchmark datasets.

Existing multi-view clustering approaches based on matrix factorization often fail to jointly capture global high-order correlations and local view-specific characteristics, and they typically suffer from instability in generating final clustering labels. To overcome these limitations, this paper presents a multi-view subspace clustering method termed dual-tensor constrained multi-view subspace clustering (DTCMVSC). Specifically, for each view, we learn an independent latent representation matrix, a projection matrix, and a basis matrix. The latent representations and projection matrices are stacked into third-order tensors, upon which tensor nuclear norm regularization is imposed to simultaneously exploit consensus structures and complementary information across views. Additionally, a consensus regularization term and adaptive view weights are introduced to align the latent representations of different views toward a unified consensus subspace. The resulting optimization problem is efficiently solved under the ADMM framework, after which a similarity matrix is constructed from the consensus representation and spectral clustering is performed to obtain the final labels. Experimental evaluations on six benchmark datasets demonstrate the superiority of DTCMVSC. Specifically, it achieves an ACC of 86.10% on CMU and an NMI of 94.17% on ORL, surpassing even the lowest-performing state-of-the-art baselines by 63.08 and 18.53 percentage points, respectively.