A SPARSE PROJECTION CLUSTERING ALGORITHM

A clustering algorithm based on Sparse Projection (SP), called Sparse Projection Clus- tering (SPC), is proposed in this letter. The basic idea is applying SP to project the observed data onto a high-dimensional sparse space, which is a nonlinear mapping with an explicit form and the K-means clustering algorithm can be therefore used to explore the inherent data patterns in the new space. The proposed algorithm is applied to cluster a complete artificial dataset and an incomplete real dataset. In comparison with the kernel K-means clustering algorithm, the proposed algorithm is more efficient.

Cluster projective synchronization of complex networks with nonidentical dynamical nodes

We investigate a new cluster projective synchronization （CPS） scheme in time-varying delay coupled complex dynamical networks with nonidentical nodes. Based on the community structure of the networks, the controllers are designed differently for the nodes in one community, which have direct connections to the nodes in the other communities and the nodes without direct connections to the nodes in the other communities. Some sufficient criteria are derived to ensure the nodes in the same group projectively synchronize and there is also projective synchronization between nodes in different groups. Particularly, the weight configuration matrix is not assumed to be symmetric or irreducible. The numerical simulations are performed to verify the effectiveness of the theoretical results.

Research and Discussion on Project Cluster Teaching Mode based on the Enterprise Level Application

Currently, the country promotes with great effort the university should the application specific education, speeds up constructing to take getting employed as the guidance modern vocational education system. In order to strengthen the vocational skill ability of student and enhance the employment competitiveness, this article proposes enterprise application-based project colony educational model. In the teaching process, the school subject knowledge education and business skills needs of the enterprise integration, the use of enterprise program teaching, so that students can not only receive professional knowledge of the system education, but also the ability of professional application of formal training and training, after graduation the students can quickly adapt to the work of the business requirements, to achieve the purpose of application-oriented teaching.

Subspace Clustering in High-Dimensional Data Streams:A Systematic Literature Review

Clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering performance.Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space.Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams.Data streams are not only high-dimensional,but also unbounded and evolving.This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams.Although many articles have contributed to the literature review on data stream clustering,there is currently no specific review on subspace clustering algorithms in high-dimensional data streams.Therefore,this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming environments.The review follows a systematic methodological approach and includes 18 articles for the final analysis.The analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data streams.The main findings relate to six elements:clustering process,cluster search,subspace search,synopsis structure,cluster maintenance,and evaluation measures.Most algorithms use a two-phase clustering approach consisting of an initialization stage,a refinement stage,a cluster maintenance stage,and a final clustering stage.The density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected microclusters.Most algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to outliers.Future work can focus on the clustering framework,parameter optimization,subspace search techniques,memory-efficient synopsis structures,explicit cluster change detection,and intrinsic performance metrics.This article can serve as a guide for researchers interested in high-dimensional subspace clustering methods for data streams.

Assessing the cost reduction potential of CCUS cluster projects of coal-fired plants in Guangdong Province in China

Carbon capture, utilization, and storage (CCUS) have garnered extensive attention as a target of carbon neutrality in China. The development trend of international CCUS projects indicates that the cluster construction of CCUS projects is the main direction of future development. The cost reduction potential of CCUS cluster projects has become a significant issue for CCUS stakeholders. To assess the cost reduction potential of CCUS cluster projects, we selected three coal-fired power plants in the coastal area of Guangdong as research targets. We initially assessed the costs of building individual CCUS projects for each plant and subsequently designed a CCUS cluster project for these plants. By comparing individual costs and CCUS cluster project costs, we assessed the cost reduction potential of CCUS cluster projects. The results show that the unit emission reduction cost for each plant with a capacity of 300 million tonnes per year is 392.34, 336.09, and 334.92 CNY/tCO_(2). By building CCUS cluster project, it could save 56.43 CNY/tCO_(2) over the average cost of individual projects (354.45 CNY/tCO_(2)) when the total capture capacity is 9 million tonnes per year (by 15.92%). Furthermore, we conducted a simulation for the scenario of a smaller designed capture capacity for each plant. We found that as the capture scale increases, the cost reduction potential is higher in the future.

A Projection Pursuit Dynamic Cluster Model Based on a Memetic Algorithm

A Projection Pursuit Dynamic Cluster(PPDC) model optimized by Memetic Algorithm(MA) was proposed to solve the practical problems of nonlinearity and high dimensions of sample data, which appear in the context of evaluation or prediction in complex systems. Projection pursuit theory was used to determine the optimal projection direction; then dynamic clusters and minimal total distance within clusters(min TDc) were used to build a PPDC model. 17 agronomic traits of 19 tomato varieties were evaluated by a PPDC model. The projection direction was optimized by Simulated Annealing(SA) algorithm, Particle Swarm Optimization(PSO), and MA. A PPDC model,based on an MA, avoids the problem of parameter calibration in Projection Pursuit Cluster(PPC) models. Its final results can be output directly, making the cluster results objective and definite. The calculation results show that a PPDC model based on an MA can solve the practical difficulties of nonlinearity and high dimensionality of sample data.

A 3 Billion Yuan Lithium Battery New Energy Vehicle Industrial Cluster Project Landed in Daye

On December 24,a new industrial partner entered the friend circle of new energy vehicle industrial cluster in Daye;a new material project with a total investment of 3 billion yuan held ground-breaking ceremony,signaling its formal landing in Daye.This Project is invested and constructed by Hubei Zhongxing New Advanced Material Co.,Ltd,the Project involves total investment of

Optimal Set Cover Formulation for Exclusive Row Biclustering of Gene Expression

The availability of large microarray data has led to a growing interest in biclustering methods in the past decade. Several algorithms have been proposed to identify subsets of genes and conditions according to different similarity measures and under varying constraints. In this paper we focus on the exclusive row biclustering problem （also known as projected clustering） for gene expression, in which each row can only be a member of a single bicluster while columns can participate in multiple clusters. This type of biclustering may be adequate, for example, for clustering groups of cancer patients where each patient （row） is expected to be carrying only a single type of cancer, while each cancer type is associated with multiple （and possibly overlapping） genes （columns）. We present a novel method to identify these exclusive row biclusters in the spirit of the optimal set cover problem. We present our algorithmic solution as a combination of existing biclustering algorithms and combinatorial auction techniques. Furthermore, we devise an approach for tuning the threshold of our algorithm based on comparison with a null model, inspired by the Gap statistic approach. We demonstrate our approach on both synthetic and real world gene expression data and show its power in identifying large span non-overlapping rows submatrices, while considering their unique nature.