Efficient Clustering Method Research Articles

Structured multi-view k-means clustering

K-means is a very efficient clustering method and many multi-view k-means clustering methods have been proposed for multi-view clustering during the past decade. However, since k-means have trouble uncovering clusters of varying sizes and densities, these methods suffer from the same performance issues as k-means. Improving the clustering performance of multi-view k-means has become a challenging problem. In this paper, we propose a new multi-view k-means clustering method that is able to uncover clusters in arbitrary sizes and densities. The new method simultaneously performs three tasks, i.e., sparse connection probability matrices learning, prototypes aligning, and cluster structure learning. We evaluate the proposed new method by 5 benchmark datasets and compare it with 11 multi-view clustering methods. The experimental results on both synthetic and real-world experiments show the superiority of our proposed method.

Auto-weighted Graph Reconstruction for efficient ensemble clustering

Ensemble clustering (EC) is an important field in data clustering, which aims to combine multiple base clustering results of a given dataset into a single consensus result. Previous studies have shown that one of the effective methods is to convert the base clustering results into a graph partitioning problem. Existing spectral ensemble clustering methods typically obtain a spectral embedding matrix for the graph and then perform k-means, separating the graph construction and clustering tasks into two distinct stages. To address these challenges, we propose a novel and efficient ensemble clustering method named Auto-weighted Graph Reconstruction for efficient ensemble clustering. This method integrates weighted base clusters, consensus distance graph construction, and clustering into a unified framework. A consensus distance graph is constructed, from which a stable and discrete label matrix is derived using spectral clustering. An effective optimization algorithm is then employed to address the resulting problem. Finally, the experimental results on eight real-world datasets verify the effectiveness and superiority of the proposed method.

Efficient subspace clustering and feature extraction via [formula omitted]-norm and [formula omitted]-norm minimization

The nuclear norm-based Latent Low-Rank Representation (LatLRR) has gained much attention due to its success in subspace clustering and feature extraction. However, it suffers from high computational costs due to the calculation of singular value decomposition for large matrices. To this end, we develop an efficient subspace clustering and feature extraction method (ESCFE) which substitutes the nuclear norm with the ℓ2,1-norm and ℓ1,2-norm respectively. Theoretically proof shows both the ℓ2,1-norm and ℓ1,2-norm can serve as the convex surrogates of the nuclear norm while can derive closed-form solutions. Furthermore, the ℓ2,1-norm (or ℓ1,2-norm) regularization promotes column (or row) structure sparsity due to the discriminative nature inherited from the ℓ1-norm. Thus the proposed ESCFE is robust to outliers in data and can extract features with joint sparsity. Extensive experiments on multiple benchmark datasets demonstrate the superiority of our method in both efficiency and effectiveness.

Efficient correntropy-based multi-view clustering with alignment discretization

Multiview clustering (MVC) has attracted considerable attention owing to its remarkable capacity to reconcile diverse information from multiple perspectives. However, traditional MVC generally has a narrow scope of application owing to its limited efficiency. Consequently, various efficient MVC (EMVC) methods have emerged recently. Despite their promising performance, these EMVC methods still have several unresolved issues: (1) They suffer from reduced effectiveness caused by representation non-alignment across views and information mismatch between stages, and (2) they fail to efficiently resist complex noises and outliers. To address these issues, we propose an efficient correntropy-based multiview clustering method with alignment discretization (ECMCAD). Specifically, a correntropy-based multipartition learning model was developed to efficiently learn view-specific robust partition-level representations. Additionally, a novel alignment discretization strategy was designed to align the learned cross-view representations into a consensus discrete indicator to integrate representation learning, representation alignment, and discrete label acquisition into a unified framework. Furthermore, an efficient alternating optimization method was developed to solve the model. Numerous experiments illustrated the superiority of the proposed method over state-of-the-art baselines.

Satellite-Terrestrial Integrated 6G: An Ultra-Dense LEO Networking Management Architecture

With the rapid development of low earth orbit (LEO) satellites, the ultra-dense LEO satellite-terrestrial integrated networking has become a promising paradigm to provide wide coverage, high capacity and flexible services for the sixth generation (6G) mobile communication networks. However, the natural heterogeneity, high dynamics, and large scale of ultradense LEO satellite networks pose new challenges to network control and management. To this end, in this article, we propose a medium earth orbit (MEO), low earth orbit (LEO) and satellite Earth stations (SESs) integrated multi-layered management architecture for the ultra-dense LEO satellite network, where global controllers and local controllers are introduced to reduce the network management complexity. With the aid of this hybrid multi-layered management framework, we can efficiently implement the network status control, mobility management, resource management, and service management in the satelliteterrestrial integrated network. To obtain proper global and local controllers with high management efficiency in the hybrid multilayered network management architecture, we further propose a novel and efficient grouping and clustering method for the ultradense LEO satellite network, where each group manager MEO satellite and cluster head (CH) LEO satellites are respectively considered as the global and local controllers of each LEO satellite group. Numerical simulations are carried out to evaluate the superiority and effectiveness of the proposed hybrid multilayered ultra-dense LEO networking management architecture.

Efficient multi-omics clustering with bipartite graph subspace learning for cancer subtype prediction

<p>Due to the complex nature and highly heterogeneous of cancer, as well as different pathogenesis and clinical features among different cancer subtypes, it was crucial to identify cancer subtypes in cancer diagnosis, prognosis, and treatment. The rapid developments of high-throughput technologies have dramatically improved the efficiency of collecting data from various types of omics. Also, integrating multi-omics data related to cancer occurrence and progression can lead to a better understanding of cancer pathogenesis, subtype prediction, and personalized treatment options. Therefore, we proposed an efficient multi-omics bipartite graph subspace learning anchor-based clustering (MBSLC) method to identify cancer subtypes. In contrast, the bipartite graph intended to learn cluster-friendly representations. Experiments showed that the proposed MBSLC method can capture the latent spaces of multi-omics data effectively and showed superiority over other state-of-the-art methods for cancer subtype analysis. Moreover, the survival and clinical analyses further demonstrated the effectiveness of MBSLC. The code and datasets of this paper can be found in <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/Julius666/MBSLC">https://github.com/Julius666/MBSLC</ext-link>.</p>

Deep Fuzzy Clustering Transformer: Learning the General Property of Corruptions for Degradation-Agnostic Multitask Image Restoration

For the sake of eliminating multiple degradations, most existing multi-task image restoration methods prefer to learn the properties of each degradation type, which is often accompanied by a bloated model size and a heavy learning burden. To tackle the aforementioned issues, we propose to treat multiple degradations uniformly to achieve degradation type-agnostic multi-task image restoration. We observe that the degradations in different spatial locations are always morphologically similar while the background sceneries vary greatly. In accordance with the above observation, we decouple the degradation features and the background features by an efficient fuzzy clustering method. The degradation features contain all the diverse degradation information, while the images are recovered from the decoupled background features. In practice, we discover a uniformity between the Fuzzy C-means algorithm and Cross-Attention and propose a Deep Fuzzy Clustering Transformer to achieve degradation type-agnostic background extraction via feature map clustering based on spatial distribution characteristics. Furthermore, to capture the spatial distribution properties of an image, an efficient global attention tree is devised to provide a global spatial receptive field for the clustering process. By virtue of the quadtree structure, the proposed global attention trees enable more efficient global modeling than existing methods. Our experimental analysis showed that the proposed method outperformed the state-of-the-art models in terms of both efficiency and performance.

Risk-constrained stochastic scheduling for energy hub: Integrating renewables, demand response, and electric vehicles

This research introduces a stochastic scheduling approach that incorporates risk constraints for an energy hub (EH), considering uncertainties related to renewable generation and load demands. The proposed method utilizes the Conditional Value at Risk (CVaR) technique to assess and quantify risks. By striking a balance between reducing operational and emissions costs and increasing risk aversion, the approach presents a trade-off. The EH comprises various components such as a wind turbine (WT), photovoltaic (PV) cells, a fuel cell power plant (FCPP), a combined heat and power generation unit (CHP), and plug-in electric vehicles (PEVs). Uncertain variables encompass factors such as wind speed, solar irradiation, different demands, and market prices. To optimize profits and enhance the consumption curve, demand response programs (DRPs) for electrical, thermal, and cooling demands are implemented. To address the uncertainties associated with input random variables, the efficient k-means data clustering method is employed. A new slime mold algorithm, based on coughing and chaos theory, has been proposed to enhance the problem's solution. The algorithm incorporates innovative operators to improve its capabilities. By utilizing the coughing mechanism and chaos theory, the algorithm explores the solution space more effectively, resulting in improved outcomes for the problem at hand. The results demonstrate significant flexibility in EH management and are extensively discussed. Simulation results indicate that integrating PEVs, FCPP, and DRPs can lead to reductions of 2 %, 7 %, and 11 % in the EH's operating costs, respectively. Furthermore, considering PEVs, FCPP, and DRPs can improve the EH's risk cost by 1.98 %, 6.7 %, and 10.5 %, respectively. Based on the numerical results, in Case 4 led to a remarkable 12.65 % reduction in operational costs while simultaneously achieving a 15.43 % decrease in emission costs, showcasing the effectiveness of the proposed approach in optimizing energy management in an energy hub system.

Optimal stochastic self-scheduling of a water-energy virtual power plant considering data clustering and multiple storage systems

As the world moves towards the integration of different water and energy resources, as well as storage systems, it is necessary to develop the conventional structure of virtual power plants (VPPs). In this work, a mixed-integer nonlinear programming (MINLP) model is established for stochastic self-scheduling problem of a new VPP structure, namely water-energy VPP. The proposed VPP, which participates in the electricity market to maximize its daily profit, aggregates different energy and water resources including wind turbines, compressed air energy storage (CAES), gas boiler, electrical boiler, absorption chiller, ice storage, water storage, and water well to supply water demand, as well as different types of energy demands such as cooling, power, and heat demands. To cope with the uncertain behavior of wind power, a scenario-based approach is employed. First, with the use of Monte Carlo simulation in MATLAB, a number of scenarios are obtained, and afterwards, K-means technique, which is a fast and efficient data clustering method, is implemented for the scenario reduction. Additionally, the impact of the consideration of CAES system on the performance of the proposed water-energy is discussed. The numerical results indicate that with the deployment of CAES system into the structure of the water-energy VPP, the expected daily profit is increased up to 4.26 %.

Incremental estimation of low-density separating hyperplanes for clustering large data sets

An efficient unsupervised method for obtaining low-density hyperplane separators is proposed. The method is based on a modified stochastic gradient descent applied on a convolution of the empirical distribution function with a smoothing kernel. Low-density hyperplanes are motivated by the fact that they avoid intersecting high density regions, and so tend to pass between high density clusters, thus separating them from one another, while keeping the individual clusters intact. Multiple hyperplanes can be combined in a hierarchical model to obtain a complete clustering solution. A simple post-processing of solutions induced by large collections of hyperplanes yields an efficient and accurate clustering method, capable of automatically selecting the number of clusters. Experiments show that the proposed method is highly competitive in terms of both speed and accuracy when compared with relevant benchmarks. Code is available in the form of an R package at https://github.com/DavidHofmeyr/iMDH.

Clustering and Annotation of T Cell Receptor Repertoires.

Immunological protection against a wide variety of pathogens is largely mediated by the diverse and dynamic T cell receptor (TCR) repertoire, a crucial component of the adaptive immune system. An encounter with infectious agents stimulates specific T cells to initiate a direct immune response to combat intruders. Hence, the TCR repertoire may conceal crucial information regarding current and past infections and might assist in the development and monitoring of vaccines. To unlock its knowledge, we describe a computational workflow involving both supervised and unsupervised machine learning techniques to analyze and annotate full TCR repertoire data. The method is explained using data from a published yellow fever virus (YFV) vaccination study in healthy individuals. The TCR repertoire of one individual is studied before and 2 weeks after vaccination, using an efficient clustering method and identification of YFV-specific TCRs.

HCP miner: an efficient heuristic-based clustering method for discovering colossal frequent patterns from high dimensional databases

HCP miner: an efficient heuristic-based clustering method for discovering colossal frequent patterns from high dimensional databases

GraphPlanner: Floorplanning with Graph Neural Network

Chip floorplanning has long been a critical task with high computation complexity in the physical implementation of VLSI chips. Its key objective is to determine the initial locations of large chip modules with minimized wirelength while adhering to the density constraint, which in essence is a process of constructing an optimized mapping from circuit connectivity to physical locations. Proven to be an NP-hard problem, chip floorplanning is difficult to be solved efficiently using algorithmic approaches. This article presents GraphPlanner, a variational graph-convolutional-network-based deep learning technique for chip floorplanning. GraphPlanner is able to learn an optimized and generalized mapping between circuit connectivity and physical wirelength and produce a chip floorplan using efficient model inference. GraphPlanner is further equipped with an efficient clustering method, a unification of hyperedge coarsening with graph spectral clustering, to partition a large-scale netlist into high-quality clusters with minimized inter-cluster weighted connectivity. GraphPlanner has been integrated with two state-of-the-art mixed-size placers. Experimental studies using both academic benchmarks and industrial designs demonstrate that compared to state-of-the-art mixed-size placers alone, GraphPlanner improves placement runtime by 25% with 4% wirelength reduction on average.

SsvEEGc: An efficient EEG clustering method using balance-constrained voting mechanism

With the increasing amount of unlabeled electroencephalography (EEG) trials in the brain–computer interface (BCI) domain, neurocognitive disorder diagnoses, and rehabilitation, etc., supervised EEG analysis that requires completely prior information of EEG labels has become unpractical. Although a few methods for unlabeled EEG clustering have recently emerged, their models or objective functions are pretty complex and time-consuming, which degrades the practicability in BCI or disease diagnosis that demands high efficiency of methods. To improve the performance of analyzing and clustering EEG trials for BCI applications, we simultaneously consider (1) the efficiency and efficacy, (2) the clustering balance and (3) the theoretical property satisfiability for partially labeled EEG trials in this paper. We propose an easily implemented and efficient EEG clustering method called ssvEEGc for partially labeled EEG trials by designing a high-efficiency and effective voting mechanism with clustering balance constraints. ssvEEGc satisfies 2 of 3 clustering theoretical properties and yields high-quality EEG clusters with low time consumption. Further, comprehensive experiments on 15 real-world EEG datasets demonstrate that ssvEEGc is superior to 11 state-of-the-art unsupervised clustering methods in terms of efficiency, effectiveness and theoretical property satisfiability. The results indicate that ssvEEGc can be applied to larger EEG datasets in biomedical signal processing and control application scenarios.

Secuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data.

Identifying cell clusters is a critical step for single-cell transcriptomics study. Despite the numerous clustering tools developed recently, the rapid growth of scRNA-seq volumes prompts for a more (computationally) efficient clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite graph representation algorithm, Secuer enjoys reduced runtime and memory usage over one order of magnitude for datasets with more than 1 million cells. Meanwhile, Secuer also achieves better or comparable accuracy than competing methods in small and moderate benchmark datasets. Furthermore, we showcase that Secuer can also serve as a building block for a new consensus clustering method, Secuer-consensus, which again improves the runtime and scalability of state-of-the-art consensus clustering methods while also maintaining the accuracy. Overall, Secuer is a versatile, accurate, and scalable clustering framework suitable for small to ultra-large single-cell clustering tasks.

An efficient topological-based clustering method on spatial data in network space

In recent years, along with the rapid development of location-based information, clustering algorithms on spatial data have been extensively applied for data insight and knowledge discovery. Existing clustering techniques usually depend on user parameters and mainly perform on the plane with Euclidean distance. This work proposes a Network Space Topological-Based Clustering (NS-TBC) algorithm for clustering network-constraint objects using a topological-based framework. This approach replaces the distance measures with topological relations for the spatial clustering problem in network space to optimize the parameters. The proposed method exploits the advantages of the ACUTE algorithm, proposed in 2016, but for network-constraint objects. The NS-TBC algorithm is applied to six datasets from Open Street Map to demonstrate its effectiveness. It outperforms the internal validation Davies–Bouldin index against the ACUTE and iNS-DBSCAN algorithms, in which iNS-DBSCAN is our state-of-art efficient clustering algorithm for network-constrained spatial data. The runtime is also carefully measured for evaluation purposes. The evaluation was performed only for two algorithms, NS-TBC, and iNS-DBSCAN, because ACUTE was not designed to work in network space. The experimental results show that the NS-TBC algorithm uses less than 50% of the computation time needed by the iNS-DBSCAN algorithm. In short, the proposed algorithm NS-TBC provided a solution to reduce the number of parameters for the iNS-DBSCAN algorithm while significantly improving the execution time and cluster quality.

МЕТОДЫ И АЛГОРИТМЫ КЛАСТЕРИЗАЦИИ ТЕКСТОВЫХ ДАННЫХ (ОБЗОР)

The article deals with one of the important tasks of artificial intelligence – machine processingof natural language. The solution of this problem based on cluster analysis makes it possibleto identify, formalize and integrate large amounts of linguistic expert information under conditionsof information uncertainty and weak structure of the original text resources obtained fromvarious subject areas. Cluster analysis is a powerful tool for exploratory analysis of text data,which allows for an objective classification of any objects that are characterized by a number offeatures and have hidden patterns. A review and analysis of modern modified algorithms for agglomerativeclustering CURE, ROCK, CHAMELEON, non-hierarchical clustering PAM, CLARAand the affine transformation algorithm used at various stages of text data clustering, the effectivenessof which is verified by experimental studies, is carried out. The paper substantiates therequirements for choosing the most efficient clustering method for solving the problem of increasing the efficiency of intellectual processing of linguistic expert information. Also, the paper considersmethods for visualizing clustering results for interpreting the cluster structure and dependencieson a set of text data elements and graphical means of their presentation in the form ofdendograms, scatterplots, VOS similarity diagrams, and intensity maps. To compare the quality ofthe algorithms, internal and external performance metrics were used: "V-measure", "AdjustedRand index", "Silhouette". Based on the experiments, it was found that it is necessary to use ahybrid approach, in which, for the initial selection of the number of clusters and the distribution oftheir centers, use a hierarchical approach based on sequential combining and averaging the characteristicsof the closest data of a limited sample, when it is not possible to put forward a hypothesisabout the initial number of clusters. Next, connect iterative clustering algorithms that providehigh stability with respect to noise features and the presence of outliers. Hybridization increasesthe efficiency of clustering algorithms. The research results showed that in order to increase thecomputational efficiency and overcome the sensitivity when initializing the parameters of clusteringalgorithms, it is necessary to use metaheuristic approaches to optimize the parameters of thelearning model and search for a global optimal solution.

Low-rank constraint bipartite graph learning

Bipartite Graph-based multi-view clustering has become an active topic recently due to its efficiency in tackling large scale multi-view data. However, most existing bipartite graph-based multi-view clustering methods have the following disadvantages: 1) the clustering performance heavily depends on the predefined bipartite graph; 2) they fail to explore the complementary information embedded in multiple bipartite graphs and spatial low-rank structure hidden in each bipartite graph. To address these issues, we propose an efficient multi-view clustering method, Learning Constraint Bipartite Graphs for Multi-view Clustering (LCBG). LCBG adaptively learns each graph S(v)∈RN×M such that it well characterizes the relationship between M (M≪N) anchors and N samples in the v-th view, and takes both the intra-view and inter-view spatial low-rank structures of the learned bipartite graphs into account by minimizing tensor Schatten p-norm and nuclear norm, respectively. Finally, an efficient algorithm, which scales linearly with the data size, is proposed to solve LCBG. Extensive experimental results on five benchmark datasets indicate our proposed LCBG is superior to the state-of-the-art methods.

Clover: tree structure-based efficient DNA clustering for DNA-based data storage.

Deoxyribonucleic acid (DNA)-based data storage is a promising new storage technology which has the advantage of high storage capacity and long storage time compared with traditional storage media. However, the synthesis and sequencing process of DNA can randomly generate many types of errors, which makes it more difficult to cluster DNA sequences to recover DNA information. Currently, the available DNA clustering algorithms are targeted at DNA sequences in the biological domain, which not only cannot adapt to the characteristics of sequences in DNA storage, but also tend to be unacceptably time-consuming for billions of DNA sequences in DNA storage. In this paper, we propose an efficient DNA clustering method termed Clover for DNA storage with linear computational complexity and low memory. Clover avoids the computation of the Levenshtein distance by using a tree structure for interval-specific retrieval. We argue through theoretical proofs that Clover has standard linear computational complexity, low space complexity, etc. Experiments show that our method can cluster 10 million DNA sequences into 50 000 classes in 10 s and meet an accuracy rate of over 99%. Furthermore, we have successfully completed an unprecedented clustering of 10 billion DNA data on a single home computer and the time consumption still satisfies the linear relationship. Clover is freely available at https://github.com/Guanjinqu/Clover.

Reliability-based multi-attribute large group decision making under probabilistic linguistic environment

This study proposes a more rational and effective multi-attribute large group decision making (MALGDM) method with probabilistic linguistic term set (PLTS) from reliability perspective. A reliability measure method is first proposed to compute the reliability degree of the PLTS, and then a normalization method is presented to normalize the ignorant PLTSs with respect to maximizing their reliability degrees. An efficient clustering method combining the opinion similarity of experts and the reliability degrees of the clusters formed is introduced. Moreover, an objective method of determining the similarity and reliability thresholds is presented. After classifying the large-scale experts, the consensus levels of clusters and the global consensus level are measured and the cluster that need to adjust information is identified based on its consensus level and reliability degree. Then, an optimization model to maximize the global consensus level and the global reliability degree is then built to obtain the evaluation values for improving the consensus levels and reliability degrees. The deviation between the expectation values of the evaluation values before and after adjustment is constrained by the parameter provided by the experts within the cluster that need adjustment. Finally, an application example of the selection of the hotel for isolating the entry personnel during the Covid-19 pandemic and some comparative analyses are provided to validate the proposed method.