In this paper, a multi-strategy improved African Vulture Optimization Algorithm (MIAVOA) is proposed address the drawback of premature convergence of the African Vulture Optimization Algorithm (AVOA). Firstly, the gaussian quasi reflection-based learning strategy is introduced, which improves the vulture initial population’s randomness and diversity. Then, the adaptive control strategy is used to enhance the search ability of the algorithm and avoid premature convergence. Furthermore, the elite candidate pooling strategy is designed in the exploitation phase, which expands the discovery fields for the optimal solution and reinforces the ability to escape from local optima. Finally, the formula of starvation factor is modified to balance the exploitative and explorative abilities of algorithm. MIAVOA is compared with seven state-of-the-art meta-heuristics on CEC 2022 and 23 classical test functions. It is observed that the proposed algorithm significant outperforms the other compared algorithms in terms of convergence and accuracy on the majority of benchmark functions. In addition, four engineering design problems and mobile robot path planning problem are utilized to evaluate the performance of MIAVOA. The experimental results demonstrate MIAVOA is effective and can achieve better applicability in real-world scenarios.

Multimodal recommendation systems have made significant progress by leveraging graph convolutional networks to integrate user behavior with item content, including images and text. However, these systems still encounter two major challenges: noise edges in interaction graphs and noise in multimodal features of items. Existing works tend to address only one type of noise problem to enhance recommendation performance. This article proposes a new Dual Denoising Multimodal Graph Recommendation (DDRec) model, designed to enhance multimodal recommendation systems by tackling both challenges simultaneously. Specifically, we design two denoising techniques: hard denoising and soft denoising. For noise edges in interaction graphs, the hard denoising method uses preference scores of user nodes and item nodes in different modality interaction graphs as edge weights and prunes edges below a certain threshold to eliminate noise. For noise in multimodal features, the soft denoising method leverages item and item semantic graph information to denoise modal features, thus obtaining modality features related to user preferences. Finally, we employ contrastive learning to compare user and item representations derived from the denoised modality interaction graphs against those from the original graph, ensuring the consistency of nodes across various views. Our comprehensive experiments across four public datasets validate the enhanced performance and effectiveness of the DDRec model.

A growing number of applications generate streaming data, making data stream mining a popular research topic. Classification-based streaming algorithms require pre-training on labeled data. Manually labeling a large number of samples in the data stream is impractical and cost-prohibitive. Stream clustering algorithms rely on unsupervised learning. They have been widely studied for their ability to effectively analyze high-speed data streams without prior knowledge. Stream clustering plays a key role in data stream mining. Currently, most data stream clustering algorithms adopt the online-offline framework. In the online stage, micro-clusters are maintained, and in the offline stage, they are clustered using an algorithm similar to density-based spatial clustering of applications with noise (DBSCAN). When data streams have clusters with varying densities and ambiguous boundaries, traditional data stream clustering algorithms may be less effective. To overcome the above limitations, this article proposes a fully online stream clustering algorithm called fast boundary peeling stream clustering (FBPStream). First, FBPStream defines a decay-based kernel density estimation (KDE). It can discover clusters with varying densities and identify the evolving trend of streams well. Then, FBPStream implements an efficient boundary micro-cluster peeling technique to identify the potential core micro-clusters. Finally, FBPStream employs a parallel clustering strategy to effectively cluster core and boundary micro-clusters. The proposed algorithm is compared with ten popular algorithms on 15 data streams. Experimental results show that FBPStream is competitive with the other ten popular algorithms.

A probabilistic load forecast that is accurate and reliable is crucial to not only the efficient operation of power systems but also to the efficient use of energy resources. In order to estimate the uncertainties in forecasting models and nonstationary electric load data, this study proposes a probabilistic load forecasting model, namely BFEEMD-LSTM-TWSVRSOA. This model consists of a data filtering method named fast ensemble empirical model decomposition (FEEMD) method, a twin support vector regression (TWSVR) whose features are extracted by deep learning-based long short-term memory (LSTM) networks, and parameters optimized by seeker optimization algorithms (SOAs). We compared the probabilistic forecasting performance of the BFEEMD-LSTM-TWSVRSOA and its point forecasting version with different machine learning and deep learning algorithms on Global Energy Forecasting Competition 2014 (GEFCom2014). The most representative month data of each season, totally four monthly data, collected from the one-year data in GEFCom2014, forming four datasets. Several bootstrap methods are compared in order to determine the best prediction intervals (PIs) for the proposed model. Various forecasting step sizes are also taken into consideration in order to obtain the best satisfactory point forecasting results. Experimental results on these four datasets indicate that the wild bootstrap method and 24-h step size are the best bootstrap method and forecasting step size for the proposed model. The proposed model achieves averaged 46%, 11%, 36%, and 44% better than suboptimal model on these four datasets with respect to point forecasting, and achieves averaged 53%, 48%, 46%, and 51% better than suboptimal model on these four datasets with respect to probabilistic forecasting.

Time-series clustering is a crucial unsupervised technique for analyzing data, commonly used in various fields, including medicine and stock analysis. However, in real-world scenarios, time-series data inevitably contain missing values, consequently reducing the efficiency of traditional clustering methods. In incomplete time series, existing clustering methods typically adopt a two-stage strategy, i.e., initially imputing missing values followed by clustering. However, this approach of separating imputation from clustering may lead to inconsistencies in the optimization objectives and increase the complexity of parameter tuning, potentially resulting in unsatisfactory clustering results. This article proposes an end-to-end deep fuzzy clustering (EEDFC) model for incomplete time series, which jointly optimizes imputation and clustering within a unified framework by integrating multiple losses. In the imputation part, an attention mechanism is integrated to tackle challenges associated with dependencies in extended sequences. In addition, an adversarial strategy is introduced to enhance the encoder's imputation and feature representation learning capability, thus reducing the error propagation from imputation to clustering. In the clustering part, EEDFC combines a feature weighting-based fuzzy clustering, which considers intracluster compactness and intercluster separateness. Furthermore, exponential distance is adopted, and feature and cluster weighting are also integrated into the Kullback–Leibler divergence loss to improve clustering performance. We conduct extensive experiments comparing our proposed model with eleven other methods across ten benchmark datasets. The experimental results demonstrate that our proposed model performs better than eleven comparative methods.

A bunch of stream clustering algorithms have been proposed recently to mine data streams generated at high speeds from hardware platforms and software applications. Density-based methods are widely used because they can handle outliers and capture clusters of arbitrary shapes. However, it is still hard to effectively identify multi-density clusters with ambiguous boundaries in a data stream. To address these limitations, this paper introduces a data stream clustering algorithm called TWStream, based on the three-way decision theory. It is a two-stage clustering algorithm based on density. In the online stage, an augmented$k$nn graph is maintained incrementally to accelerate the update of the$k$nn graph. In the offline stage, TWStream introduces the concept of boundary confidence to detect cluster boundaries efficiently and reveal potential cores of clusters. It integrates the skewness and sparsity of the data distribution, as well as the evolving trend of the stream.In the next step, a micro-cluster-based three-way clustering strategy is applied to reconstruct latent clusters. It improves the clustering quality of boundary-ambiguous clusters in a stream using a mutual reachability-based clustering approach and a three-way assignment approach. The proposed algorithm is compared with 9 competitors on 15 data streams. Experimental results show TWStream achieves competitive performance, verifying its effectiveness. The source code of the proposed TWStream can be available athttps://github.com/Du-Team/TWStream.

Three-way clustering has been an active research topic in the field of cluster analysis in recent years. Some efforts are focused on the technique due to its feasibility and rationality. We observe, however, that the existing three-way clustering algorithms struggle to obtain more information and limit the fault tolerance excessively. Moreover, although the one-step three-way allocation based on a pair of fixed, global thresholds is the most straightforward way to generate the three-way cluster representations, the clusters derived from a pair of global thresholds cannot exactly reveal the inherent clustering structure of the dataset, and the threshold values are often difficult to determine beforehand. Inspired by sequential three-way decisions, we propose an algorithm, called multistep three-way clustering (M3W), to address these issues. Specifically, we first use a progressive erosion strategy to construct a multilevel structure of data, so that lower levels (or external layers) can gather more available information from higher levels (or internal layers). Then, we further propose a multistep three-way allocation strategy, which sufficiently considers the neighborhood information of every eroded instance. We use the allocation strategy in combination with the multilevel structure to ensure that more information is gradually obtained to increase the probability of being assigned correctly, capturing adaptively the inherent clustering structure of the dataset. The proposed algorithm is compared with eight competitors using 18 benchmark datasets. Experimental results show that M3W achieves superior performance, verifying its advantages and effectiveness.

Data consolidation is a challenging issue in data integration. The usefulness of data increases when it is linked and fused with other data from numerous (Web) sources. The promise of Big Data hinges upon addressing several big data integration challenges, such as record linkage at scale, real-time data fusion, and integrating Deep Web. Although much work has been conducted on these problems, there is limited work on creating a uniform, standard record from a group of records corresponding to the same real-world entity. We refer to this task as record normalization. Such a record representation, coined normalized record, is important for both front-end and back-end applications. In this paper, we formalize the record normalization problem, present in-depth analysis of normalization granularity levels (e.g., record, field, and value-component) and of normalization forms (e.g., typical versus complete). We propose a comprehensive framework for computing the normalized record. The proposed framework includes a suit of record normalization methods, from naive ones, which use only the information gathered from records themselves, to complex strategies, which globally mine a group of duplicate records before selecting a value for an attribute of a normalized record. We conducted extensive empirical studies with all the proposed methods. We indicate the weaknesses and strengths of each of them and recommend the ones to be used in practice.

Compared with Support Vector Machine (SVM) that has shown success in classification tasks, Support Vector Clustering (SVC) is not widely viewed as a competitor to popular clustering algorithms. The reason is easy to state that classical SVC is of high cost and moderate performance. In spite of ever-appearing variants of SVC, they fail in solving two problems well. Focusing on these two problems, this paper proposes a Shrunk Support Vector Clustering (SSVC) algorithm that makes an effort to address two difficulties simultaneously. In the optimization piece SSVC pursues a shrunk hypersphere in feature space that only dense-region data are included in. In the labeling piece of SSVC, a new labeling approach is designed to cluster support vectors firstly, and then label other data. The development of the shrunk hypersphere is implemented by optimizing a strongly convex objective, which can be converted to a linear equation system. A fast training method is given to reduce the heavy computation burden that is necessary in SVC to solve a quadratic optimization problem. The new labeling approach is based on geometric nature of the shrunk model and works in a simple but informed way. That removes the randomness encoded in SVC labeling piece and then improves clustering accuracy. Experiments indicate SSVC's better performance and efficiency than its peers and much appealing facility compared with the state of the art.

As an important supporting technology of Market Intelligence (MI), Web data integration is facing new challenges, such as the integrity of data acquisition, the quality of data extraction and data consolidation. To solve such problems, we propose an MI-oriented web data integration system (MI-WDIS), which achieves excellent performances in integrating Surface Web and Deep Web data with much less manual work. Based on MI-WDIS, we have developed a platform for intelligent analysis of job data. The platform collects tens of thousands of job data daily and provides personalized services for job seekers through diversified channels. Besides, it provides other advanced services, including intelligence analysis, automatic monitoring and alerting, for various organizations, such as enterprises, training institutions and recruitment agencies.

With the rapid development of the Internet, the Web is becoming the largest information repository of the world. Major efforts have been made in order to integrate the data of a specific domain on the Web. The traditional methods are largely done by few of system administrators which do not adapt to web scale. The construction of a web domain data integration system (WDDIS) becomes an urgent task The paper describes a new idea which asks the users to help the builders incrementally build WDDIS. It proposes an architecture of WDDIS and describes the mechanism of user collaboration. The approach shifts the enormous endeavors from the producers to the consumers which will promote WDDIS to be constructed quickly and effectively.

With the development of Internet, there are more and more Web service related to the travel. How to make these Web services work cooperatively so as to provide the travel schedule that satisfy the user 's requirement is the problem of the process study. In this paper, we propose a new process model facing individuals with travel as background. A new semantic description model is firstly put forward. Furthermore, we provide a method with which available services can be chosen and travel schedule automatically generated according to user 's requirements. The feasibility of this method is verified through its application to virtual travel agency.

When Web services are used to coalesce around the distributed applications, one prominent solution to manage and coordinate Web services is the use of process management. Many researches have been done to deal with automatic discovery and composition issues of Web services. However, the problem of how to run a process that is composed of distributed services is seldom considered. In this paper, based on our previous work, the smart process-based application model (SPM), we formalize smart process (SP) using algebra CCS, and discuss the benefits. In particular, we propose goal-based algorithms to run Smart Process.

Virtual enterprise has been a topic of increasing interests in recent years meanwhile parts library sharing among participant enterprises has become a challenge. This paper analyzes its two demands. one is dynamic and open; the other is the share of domain knowledge. Based on these requirements, it proposes a framework for sharing parts library in virtual enterprise. The framework uses web service technologies to implement the first requirement and ontology technologies to achieve the second one. The analysis indicates that it can improve the flexibility and maintainability of the share of parts library among collaborating members and facilitate the rapid development of product to meet the market demands. At last, an example of parts library description in OWL is given.