Aiming at the problems of PBFT algorithm of consortium blockchain, such as high communication overhead, low consensus efficiency, and random selection of leader nodes, an optimized algorithm of PBFT is proposed. Firstly, the algorithm improves C4.5 and introduces weighted average information gain to overcome the mutual influence between conditional attributes and improve the classification accuracy. Then classify the nodes with improved C4.5, and select the ones with a high trust level to form the main consensus group. Finally, the integral voting mechanism is introduced to determine the leader node. Experimental results show that compared with traditional PBFT algorithm, the communication times of the improved PBFT algorithm are reduced greatly, which effectively alleviates the problem that the number of nodes in traditional PBFT algorithm increases and the traffic volume is too large, and significantly reduces the probability of the leader node doing evil and improves the consensus efficiency.

This study proposes an improved Byzantine fault-tolerant consensus RB-BFT based on the reputation model to address the problems of low reliability of primary nodes and high communication complexity in the practical Byzantine algorithm (PBFT). First, this algorithm establishes a dynamic reputation model of nodes to distinguish honest and malicious nodes in the system, lowering the likelihood of malicious nodes being chosen as primary nodes and increasing the dependability of primary nodes. Second, the algorithm introduces supervisory nodes for information supervision while reducing the centrality of the system. Finally, this algorithm improves the consistency protocol of the PBFT algorithm by optimizing the process of mutual communication between nodes in the preparation and commitment phases, which reduces the algorithm communication complexity from O(n²) to O(n). Theoretical and practical studies reveal that the RB-BFT algorithm enhances performance and reliability greatly.

Multiview clustering has become a research hotspot in recent years due to its excellent capability of heterogeneous data fusion. Although a great deal of related works has appeared one after another, most of them generally overlook the potentials of prior knowledge utilization and progressive sample learning, resulting in unsatisfactory clustering performance in real-world applications. To deal with the aforementioned drawbacks, in this article, we propose a semisupervised progressive representation learning approach for deep multiview clustering (namely, SPDMC). Specifically, to make full use of the discriminative information contained in prior knowledge, we design a flexible and unified regularization, which models the sample pairwise relationship by enforcing the learned view-specific representation of must-link (ML) samples (cannot-link (CL) samples) to be similar (dissimilar) with cosine similarity. Moreover, we introduce the self-paced learning (SPL) paradigm and take good care of two characteristics in terms of both complexity and diversity when progressively learning multiview representations, such that the complementarity across multiple views can be squeezed thoroughly. Through comprehensive experiments on eight widely used image datasets, we prove that the proposed approach can perform better than the state-of-the-art opponents.

Multi-view clustering has become a research hotspot in recent decades because of its effectiveness in heterogeneous data fusion. Although a large number of related studies have been developed one after another, most of them usually only concern with the characteristics of the data themselves and overlook the inherent connection among samples, hindering them from exploring structural knowledge of graph space. Moreover, many current works tend to highlight the compactness of one cluster without taking the differences between clusters into account. To track these two drawbacks, in this article, we propose a graph structure aware contrastive multi-view clustering (namely, GCMC) approach. Specifically, we incorporate the well-designed graph autoencoder with conventional multi-layer perception autoencoder to extract the structural and high-level representation of multi-view data, so that the underlying correlation of samples can be effectively squeezed for model learning. Then the contrastive learning paradigm is performed on multiple pseudo-label distributions to ensure that the positive pairs of pseudo-label representations share the complementarity across views while the divergence between negative pairs is sufficiently large. This makes each semantic cluster more discriminative, i.e., jointly satisfying intra-cluster compactness and inter-cluster exclusiveness. Through comprehensive experiments on eight widely-known datasets, we prove that the proposed approach can perform better than the state-of-the-art opponents.

Financial prediction is an important research field in financial data time series mining. There has always been a problem of clustering massive financial time series data. Conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several financial forecasting models. In this paper, a new hybrid algorithm is proposed based on Optimization of Initial Points and Variable-Parameter Density-Based Spatial Clustering of Applications with Noise (OVDBCSAN) and support vector regression (SVR). At the initial point of optimization, ε and MinPts, which are global parameters in DBSCAN, mainly deal with datasets of different densities. According to different densities, appropriate parameters are selected for clustering through optimization. This algorithm can find a large number of similar classes and then establish regression prediction models. It was tested extensively using real-world time series datasets from Ping An Bank, the Shanghai Stock Exchange, and the Shenzhen Stock Exchange to evaluate accuracy. The evaluation showed that our approach has major potential in clustering massive financial time series data, therefore improving the accuracy of the prediction of stock prices and financial indexes.

For the current Intellectual Property (IP) transaction scenario, consensus nodes need to simultaneously consensus transactions of the same transaction type, resulting in low consensus efficiency, accuracy, and reliability, which seriously hinders the development of intellectual property. Based on the consortium chain, this paper proposes a secure and efficient blockchain-distributed consensus algorithm, ST-PBFT (Shard Transaction Practical Byzantine Fault Tolerance), applied to the IP transaction scenario. The main contributions of ST-PBFT include the following: first, a grouping method based on the principle of consistent hashing is proposed to group consensus nodes, and nodes group consensus, which reduces the complexity of communication. Second, the transaction consensus group can process IP transactions in parallel, which improves the throughput of the algorithm. Third, a node reputation evaluation model is proposed, which can prevent byzantine nodes from being repeatedly elected as primary nodes. The experimental results show that ST-PBFT can significantly improve the consensus efficiency and reliability and reduce consensus latency.