Proposed Clustering Algorithm Research Articles

Ameliorate the performance using soft computing approaches in wireless networks

<span lang="EN-US">Wireless sensor networks are an innovative and rapidly advanced network occupying the broad spectrum of wireless networks. It works on the principle of “use with less expense, effort and with more comfort.” In these networks, routing provides efficient and effective data transmission between different sources to access points using the clustering technique. This work addresses the low-energy adaptive clustering hierarchy (LEACH) protocol’s main backdrop of choosing head nodes based on a random value. In this, the soft computing methods are used, namely the fuzzy approach, to overcome this barrier in LEACH. Our approach’s primary goal is to extend the network lifetime with efficient energy consumption and by choosing the appropriate head node in each cluster based on the fuzzy parameters. The proposed clustering algorithm focused on two fuzzy inference structures, namely Mamdani and Sugeno fuzzy logic models in two scenarios, respectively. We compared our approach with four existing works, the conventional LEACH, LEACH using the fuzzy method, multicriteria cluster head delegation, and fuzzy-based energy efficient clustering approach (FEECA) in wireless sensor network. The proposed scenario based fuzzy LEACH protocol approaches are better than the four existing methods regarding stability, network survivability, and energy consumption.</span>

An Effective Fuzzy Clustering of Crime Reports Embedded by a Universal Sentence Encoder Model

Crime reports clustering is crucial for identifying and preventing criminal activities that frequently happened in society. In the proposed work, named entities in a report are recognized to extract the crime-related phrases and subsequently, the phrases are preprocessed by applying stopword removal and lemmatization operations. Next, the module of the universal encoder model, called the transformer, is applied to extract phrases of the report to get a sentence embedding for each associated sentence, aggregation of which finally provides the vector representation of that report. An innovative and efficient graph-based clustering algorithm consisting of splitting and merging operations has been proposed to get the cluster of crime reports. The proposed clustering algorithm generates overlapping clusters, which indicates the existence of reports of multiple crime types. The fuzzy theory has been used to provide a score to the report for expressing its membership into different clusters, and accordingly, the reports are labelled by multiple categories. The efficiency of the proposed method has been assessed by taking into account different datasets and comparing them with other state-of-the-art approaches with the help of various performance measure metrics.

Accuracy and Cluster Analysis of 5.3 GHz Indoor and 285 MHz Semi-urban MIMO LOS and NLOS Propagation Multipaths

Over the past decade, several studies have been conducted to discover a better-performing multipath clustering technique. Developing a multipath clustering technique with better accuracy performance is a big challenge considering the varying properties of the multipath propagations that change over time. In this study, several clustering techniques have been investigated and compared to the newly-developed technique for performance analysis. Using the Jaccard score as a metric for the accuracy of grouping correctly the wireless multipaths, the performance of the different clustering techniques has been determined and compared to the newly-developed technique. The proposed clustering algorithm shows improved performance in the indoor channel scenarios but needs further investigation in the semi-urban environment.

이상치를 고려한 딥러닝 기반 웨이퍼 맵 결함 패턴 클러스터링

Purpose: Most clustering algorithms perform clustering without considering outliers. However, when performing clustering, outliers often degrade performance. Therefore, we propose a clustering method that can consider outliers.BRMethods: The proposed deep learning-based algorithm identifies outliers using the predicted class distribution and performs clustering based on only normal data.BRResults: The proposed algorithm achieved superior performance compared to the existing clustering algorithm. Based on the proposed clustering algorithm, the wafer maps were clustered while finding outliers in the patterns existing on them.BRConclusion: In this paper, we propose a deep learning-based clustering algorithm considering outliers (DCCO) that simultaneously performs clustering and abnormal pattern detection.

Improving Density Peaks Clustering through GPU acceleration

Density Peaks Clustering (DPC) is a recently proposed clustering algorithm that has distinct advantages over existing clustering algorithms, which has already been used in a wide range of applications. However, DPC requires computing the distance between every pair of input points, therefore incurring quadratic computation overhead, which is prohibitive for large data sets. To address this efficiency problem, we propose to use GPU to accelerate DPC. We exploit a spatial index structure VP-Tree to efficiently maintain the data points and propose a GPU-friendly parallel VP-Tree construction algorithm. Based on the constructed VP-Tree, we propose a GPU-Accelerated DPC algorithm GDPC, in which the all-pair computation in DPC is greatly accelerated. Furthermore, in order to process dynamic evolving datasets, we propose an incremental GDPC algorithm, Incremental GDPC. Our results show that GDPC can achieve over 5.3-148.9X acceleration compared to the state-of-the-art GPU-based, multicore-based, and distributed DPC implementations, a 2.3-40.5X acceleration compared to the state-of-the-art incremental DPC algorithm.

Clustering and optimising regional segregated resource allocation networks

Policymakers and officials worldwide are making more stringent environmental norms and waste disposal policies to encourage industries to move towards cleaner production. One of the main challenges that industries face moving towards cleaner production is the adoption of different strategies for optimising their resource utilisation and waste reduction economically. This is particularly challenging for large-scale industries or a group of industrial plants located in an industrial region. This paper presents a novel approach to economic resource optimisation focussed mainly on large-scale industries, different industrial plants located in the vicinity of each other, or an industrial symbiosis network. In this work, a clustering algorithm is developed to segregate the given plants into different clusters based on the concept of load deficits and surpluses of each plant. The concept ideally allows only the plants with surpluses to send out their unused sources and plants with deficits to only receive external sources/resources. The clusters are formed based on the distances between plants, which in turn helps in saving transportation and communication costs. The clustered plants are then easy to optimise and manage for resource and cost optimality. The applicability of the proposed clustering algorithm is demonstrated using two case studies from the domain of water recycling networks containing multiple contaminants with detailed network design, highlighting the importance of clustering in an industrial symbiosis network. It is observed that directing the excess flows from one plant to other plants in the same cluster can save a considerable amount of fresh resources. It implies that in the broader aspect, the developed methodology can address the optimisation of economic resources and can aid in the better management of overall resources for a large-scale industrial symbiosis network.

A Region-Based Collaborative Management Scheme for Dynamic Clustering in Green VANET

Green Vehicular Ad-hoc Network (VANET) is a newly-emerged research area that focuses on reducing harmful impacts of vehicular communication equipment on the natural environment. Recent studies have shown that grouping vehicles into clusters for green communications in VANETs can significantly improve networking efficiency and reduce infrastructure costs. As a dynamic network system, maintaining the network connectivity and reducing the communication overlap are two critical challenges for green VANET clustering. However, most existing work studies connectivity and overlap separately, lacking a deep understanding of the relationship between them. To address this issue, we present a comprehensive analysis that jointly considers the two critical factors in one model. Specifically, we first design a state resemblance prediction (SRP) model based on the historical trajectory feature relevance between vehicles; Combined with the SRP model, we propose the region-based collaborative management scheme (RCMS) to establish the dynamic clustering; Lastly, we take extensive experiments to verify the region-based collaborative management scheme for dynamic clustering. The results demonstrate that the proposed clustering algorithm can achieve high networking efficiency and better communication stability.

An improved Fuzzy based GWO algorithm for predicting the potential host receptor of COVID-19 infection

Coronavirus disease (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and has infected millions worldwide. SARS-CoV-2 spike protein uses Angiotensin-converting enzyme 2 (ACE2) and Transmembrane serine protease 2 (TMPRSS2) for entering and fusing the host cell membrane. However, interaction with spike protein receptors and protease processing are not the only factors determining coronaviruses’ entry. Several proteases mediate the entry of SARS-CoV-2 virus into the host cell. Identifying receptor factors helps understand tropism, transmission, and pathogenesis of COVID-19 infection in humans. The paper aims to identify novel viral receptor or membrane proteins that are transcriptionally and biologically similar to ACE2 and TMPRSS2 through a fuzzy clustering technique that employs the Grey wolf optimizer (GWO) algorithm for finding the optimal cluster center. The exploratory and exploitation capability of GWO algorithm is improved by hybridizing mutation and crossover operators of the evolutionary algorithm. Also, the genetic diversity of the grey wolf population is enhanced by eliminating weak individuals from the population. The proposed clustering algorithm’s effectiveness is shown by detecting novel viral receptors and membrane proteins associated with the pathogenesis of SARS-CoV-2 infection. The expression profiles of ACE2 protein and its co-receptor factor are analyzed and compared with single-cell transcriptomics profiling using the Seurat R toolkit, mass spectrometry (MS), and immunohistochemistry (IHC). Our advanced clustering method infers that cell that expresses high ACE2 level are more affected by SARS-CoV-infection. So, SARS-CoV-2 virus affects lung, intestine, testis, heart, kidney, and liver more severely than brain, bone marrow, skin, spleen, etc.We have identified 58 novel viral receptors and 816 membrane proteins, and their role in the pathogenicity mechanism of SARS-CoV-2 infection has been studied. Besides, our study confirmed that Neuropilins (NRP1), G protein-coupled receptor 78 (GPR78), C-type lectin domain family 4 member M (CLEC4M), Kringle containing transmembrane protein 1 (KREMEN1), Asialoglycoprotein receptor 1 (ASGR1), A Disintegrin and metalloprotease 17 (ADAM17), Furin, Neuregulin-1,(NRG1), Basigin or CD147 and Poliovirus receptor (PVR) are the potential co-receptors of SARS-CoV-2 virus. A significant finding is that heparin derivative glycosaminoglycans could block the replication of SARS-CoV-2 virus inside the host cytoplasm. The membrane protein N-Deacetylase/N-Sulfotransferase-2 (NDST2), Extostosin protein (EXT1, EXT2, and EXT3), Glucuronic acid epimerase (GLCE), and Xylosyltransferase I, II (XYLT1, XYLT2) could act as the therapeutic target for inhibiting the spread of SARS-CoV-2 infection. Drugs such as carboplatin and gemcitabine are effective in such situations.

Performance-Aware Green Algorithm for Clustering of Underwater Wireless Sensor Network Based on Optical Signal-to-Noise Ratio

Underwater wireless sensor network (UWSN) has limited bandwidth, long propagation delays, and unrechargeable batteries, which forces the development of techniques minimizing the energy consumption of UWSNs. One solution is clustering that outperforms in terms of energy optimization and, in addition, can also provide scalability and data reliability. The development of clustering algorithms depends on UWSN architectures. In our case, we have proposed a hybrid UWSN network architecture named ACOP-UWSN (ACoustic OPtical Underwater Wireless Sensor Network), which aims for reliable data transmission at the fastest speed with higher data rates and minimized propagation delays. In ACOP-UWSN, optical sensors sense data, whereas acoustic sensors only relay data towards the surface buoy. Clustering of all these nodes may result in unnecessary wastage of the node’s energy and memory. Therefore, we undergo clustering of only optical sensors, as comparatively, sensing and processing of data consume maximum energy and memory than relaying data. In the proposed clustering algorithm, the criteria for cluster head selection are OSNR (optical signal-to-noise ratio) and residual energy. The mathematical derivation of OSNR for underwater wireless optical communication is a key part of the algorithm. The results achieved using the simulation tool show that our proposed OSNR-based energy efficient clustering algorithm (OECA) is efficient in terms of energy and as well data reliability as compared to state of art. The quantitative analysis of the results showed that the total energy consumed for the existing scheme is 450 J, which is more as compared to 400 J of OECA. Similarly, the residual energy of nodes using OECA is 1% greater than the existing scheme. To our knowledge, research on the clustering of optical sensors in UWSN is least available, and we are among the few to work on an OSNR-based clustering algorithm for underwater optical nodes.

A New Clustering Algorithm With Preference Adjustment Cost to Reduce the Cooperation Complexity in Large-Scale Group Decision Making

In large-scale group decision making (LSGDM), appropriate clustering analysis is important to consensus reaching since it can reduce the interactive complexity among individuals. According to the traditional clustering method, a conflict may arise between the consensus reaching levels and total adjustment costs within clusters when individuals have different unit adjustment cost, which reflects their willingness to make concessions. Since this conflict may aggravate the consensus complexity, we propose a new <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering method that considers both preferences and the preference adjustment cost. The preference adjustment cost is attached to preferences with a parameter that can be determined by balancing this conflict. Because of such conflict, the proposed clustering algorithm can improve the similarity of intracluster individuals on the preference adjustment cost by offsetting some acceptable consensus reaching levels within clusters. According to the proposed clustering algorithm, individuals who have both similar preferences and adjustment willingness are classified into the same clusters. In this way, the moderator can provide similar compensation strategies for intracluster individuals, which will decrease the adjustment complexity. A practical case study of team construction examines the application of the proposed algorithm, and the related comparative analysis shows that it is convenient for managers to persuade individuals to reach a consensus under the improved clustering results.

A New Clustering Method Based on the Inversion Formula

Data clustering is one area of data mining that falls into the data mining class of unsupervised learning. Cluster analysis divides data into different classes by discovering the internal structure of data set objects and their relationship. This paper presented a new density clustering method based on the modified inversion formula density estimation. This new method should allow one to improve the performance and robustness of the k-means, Gaussian mixture model, and other methods. The primary process of the proposed clustering algorithm consists of three main steps. Firstly, we initialized parameters and generated a T matrix. Secondly, we estimated the densities of each point and cluster. Third, we updated mean, sigma, and phi matrices. The new method based on the inversion formula works quite well with different datasets compared with K-means, Gaussian Mixture Model, and Bayesian Gaussian Mixture model. On the other hand, new methods have limitations because this one method in the current state cannot work with higher-dimensional data (d &gt; 15). This will be solved in the future versions of the model, detailed further in future work. Additionally, based on the results, we can see that the MIDEv2 method works the best with generated data with outliers in all datasets (0.5%, 1%, 2%, 4% outliers). The interesting point is that a new method based on the inversion formula can cluster the data even if data do not have outliers; one of the most popular, for example, is the Iris dataset.

Learning-Based User Clustering in NOMA-Aided MIMO Networks With Spatially Correlated Channels

This paper considers the integration of non-orthogonal multiple access (NOMA) into massive multi-input multi-output (MIMO) systems for downlink transmission. We consider the joint design of user clustering, transmit beamforming, and power allocation to minimize the total transmit power while meeting the signal-to-interference-and-noise ratio targets. We decompose this challenging mixed-integer programming problem into three separate subproblems to solve. We propose a low-complexity learning-based user clustering algorithm, which is a modified version of mean shift clustering with a new channel correlation based clustering metric. The proposed clustering algorithm determines the clusters to trade-off between spatial dimension and power dimension offered by respective MIMO and NOMA for user multiplexing. We then design zero-forcing transmit beamformers to eliminate inter-cluster interference and optimize power allocation to minimize the total transmit power. We provide two case studies for both co-located and distributed massive MIMO systems in spatially highly correlated prorogation environments. Simulation results show that our proposed algorithm forms NOMA clusters based on the available degrees of freedom in the system to effectively use both spatial and power dimensions, which results in a substantial performance improvement over MIMO-only methods or other existing clustering methods in such environments.

Trust Cop-Kmeans Clustering Analysis and Minimum-Cost Consensus Model Considering Voluntary Trust Loss in Social Network Large-Scale Decision-Making

Social network large-scale decision-making (SN-LSDM) has attracted much attention in the field of decision science. Clustering and consensus building are two important processes for solving SN-LSDM problems. Traditional clustering methods are typically based on the opinion similarity. When extending decision-making to social network contexts, we argue that trust relationships among decision-makers (DMs) serve as a reliable resource for clustering. This study proposes a trust Cop-Kmeans clustering algorithm, which is a semisupervised clustering technique using prior knowledge of trust constraints generated by trust relationships. The consensus-reaching process (CRP) is an effective tool for reducing differences of opinion. In general, the costs and resources associated with the CRP are limited. Therefore, minimum-cost consensus (MCC) models have been developed and used widely in various group decision-making contexts. As an important resource influencing decision-making, trust provides a common-sense perception that the opinion of a DM with a high trust degree is considered to be widely recognized by others. We hold that the DM with high trust and low consensus can reduce the consensus cost by voluntarily losing some trust. Consequently, an improved MCC model considering voluntary trust loss is developed. Finally, a numerical example is presented to illustrate the feasibility of the proposed clustering algorithm and consensus model. A comparative analysis is implemented to explore the influence of the trust constraint on clustering and the influence of trust loss on the CRP.

Framework for Implementing Load Balancing Within Cluster in Underground Mining Environments

Abstract: Underground mines are more hazardous in nature and always contain unsafe for their workers. Underground mining causes huge losses due to accidents and disaster. There is the need to develop an effective, secure, reliable and low-cost surveillance and safety system in underground mine environments. Wireless Sensor Networks (WSNs) are increasingly being used for such application. Since wireless sensor nodes are highly energy constrained devices and they have limited battery life due to various constraints of sensor nodes. Therefore, optimal usage of node energy is the major challenge in wireless sensor networks. Clustering of sensor nodes is an effective method to use the node energy optimally and prolong lifetime of such energy constrained wireless sensor networks. Here we propose a framework for load balancing within clusters in the underground mine environment. The proposed clustering algorithm ensures balanced size cluster formation and it has been proposed to achieve balanced energy consumption among nodes. This project aims to give a secure and efficient communication for the underground are using these respective sensors and here a concept of cluster and their rotation is implemented so that the loss of signals can be restricted and helps in a secure communication

A new robust fuzzy clustering framework considering different data weights in different clusters

In conventional fuzzy C-means clustering algorithms, each data and each feature are treated equally, the clustering performance is sensitive to the noise points; in existing weighting clustering algorithms, few studies have focus on data weighting and feature weighting simultaneously, besides, the same data in different clusters is treated equally. To address this issue, in this paper, taking the different data weights in different clusters and the different feature weights in different clusters into consideration, we present a new robust fuzzy C-means clustering framework. For the first time, we propose a whole new idea that the same data in different clusters should have different importance, the different data in a cluster should have different importance, as well; By the new data weighting method, the proposed clustering algorithm can weaken the impact of noise points on the formation of each clustering center, which could enhance the robustness of clustering; to stimulate more data and more features to take part in the process of clustering and to avoid overfitting, we add l2-norm regularization of the data weights and l2-norm regularization of the feature weights to the objective function. Then, based on the presented objective function, we get the scientific update rules of the different data weights in different clusters, the different feature weights in different clusters, the membership degrees, and the cluster centers, during each iteration. To assess the performance of the new fuzzy C-means framework, experimental verifications on synthetic dataset and real-world datasets are conducted, experimental results have shown that the new algorithm can achieve better clustering performances in comparison to other related clustering methods.

An Improved Supervoxel Clustering Algorithm of 3D Point Clouds for the Localization of Industrial Robots

Supervoxels have a widespread application of instance segmentation on account of the merit of providing a highly approximate representation with fewer data. However, low accuracy, mainly caused by point cloud adhesion in the localization of industrial robots, is a crucial issue. An improved bottom-up clustering method based on supervoxels was proposed for better accuracy. Firstly, point cloud data were preprocessed to eliminate the noise points and background. Then, improved supervoxel over-segmentation with moving least squares (MLS) surface fitting was employed to segment the point clouds of workpieces into supervoxel clusters. Every supervoxel cluster can be refined by MLS surface fitting, which reduces the occurrence that over-segmentation divides the point clouds of two objects into a patch. Additionally, an adaptive merging algorithm based on fusion features and convexity judgment was proposed to accomplish the clustering of the individual workpiece. An experimental platform was set up to verify the proposed method. The experimental results showed that the recognition accuracy and the recognition rate in three different kinds of workpieces were all over 0.980 and 0.935, respectively. Combined with the sample consensus initial alignment (SAC-IA) coarse registration and iterative closest point (ICP) fine registration, the coarse-to-fine strategy was adopted to obtain the location of the segmented workpieces in the experiments. The experimental results demonstrate that the proposed clustering algorithm can accomplish the localization of industrial robots with higher accuracy and lower registration time.

An Ensemble Clustering Approach (Consensus Clustering) for High-Dimensional Data

Due to the plurality of irrelevant attributes, sparse distribution, and complicated calculations in high-dimensional data, traditional clustering algorithms, such as K-means, do not perform well on high-dimensional data. To address the clustering problem of high-dimensional data, this paper studies an integrated clustering method for high-dimensional data. A method of subspace division based on minimum redundancy is proposed to solve the problem of subspace division of high-dimensional data; subspace division is improved by using the K-means algorithm. Additionally, this method uses mutual information between the characteristic variables of the data to replace the calculation in the K-means algorithm. The distance between the characteristic variables of the data is used to divide the data into subspaces according to the mutual information values between the characteristic variables of the data. To achieve high clustering accuracy and diversity based on clustering requirements, this paper uses a genetic algorithm as the consistency integration function. The fitness function is designed according to the clustering fusion target, and the selection operator is designed according to the maximum number of overlapping elements in the base clustering. The experimental results show that the clustering algorithm proposed in this paper outperforms other methods on most datasets and is an effective clustering integration algorithm. The proposed clustering algorithm is compared with other commonly used clustering fusion algorithms on datasets to prove the advantages of the proposed algorithm.

FPCC: Fast point cloud clustering-based instance segmentation for industrial bin-picking

Instance segmentation is an important pre-processing task in numerous real-world applications, such as robotics, autonomous vehicles, and human–computer interaction. Compared with the rapid development of deep learning for two-dimensional (2D) image tasks, deep learning-based instance segmentation of 3D point cloud still has a lot of room for development. In particular, distinguishing a large number of occluded objects of the same class is a highly challenging problem, which is seen in robotic bin-picking. In a usual bin-picking scene, many identical objects are stacked together and the model of the objects is known. Thus, the semantic information can be ignored; instead, the focus in the bin-picking is put on the segmentation of instances. Based on this task requirement, we propose a Fast Point Cloud Clustering (FPCC) for instance segmentation of industrial bin-picking scene. FPCC includes a network named FPCC-Net and a fast clustering algorithm. FPCC-Net extracts features of each point and infers geometric center points of each instance simultaneously. After that, the proposed clustering algorithm clusters the remaining points to the closest geometric center in feature embedding space. Experiments show that FPCC also surpasses the existing works in bin-picking scenes and is more computationally efficient. Our code and data are available at (https://github.com/xyjbaal/FPCC).

Clustering Methods for Particle Filters With Gaussian Mixture Models

New clustering methods are proposed to develop novel particle filters with Gaussian mixture models (PFGMM). In the PFGMM, the propagated samples are clustered to recover a Gaussian mixture model (GMM) using a clustering algorithm, which plays a fundamental role in the filter's performance. Two clustering methods are introduced that simultaneously minimize the covariance of each of the GMM components and maximize the likelihood function. Under the scenarios considered in this paper, it is shown through numerical simulation that the PFGMMs with the proposed clustering algorithms lead to better performance than the PFGMM employing the K-means or the expectation-maximization (EM) algorithms as well as the regularized particle filter (RPF) and the Gaussian sum particle filter (GSPF).

Modeling vague spatiotemporal objects based on interval type-2 fuzzy sets

Fuzziness is an inherent property of geographical phenomena and the processes of data acquisition, processing, and analysis often introduce uncertainty. Existing methods predominantly use fuzzy set (FS) theory to capture the fuzziness of geographical phenomena as fuzzy spatial objects. However, this approach has a conceptual confusion regarding fuzziness, uncertainty, and vagueness, and the membership degree is expressed using accurate values that ignore uncertainty. Furthermore, FS-based methods lack a vague temporal descriptor. Herein, a vague-spatiotemporal-object model based on the interval type-2 FS theory is proposed to express the vagueness of spatiotemporal objects. To verify the feasibility and superiority of the proposed method, the fuzzy and vague clustering algorithm was used to classify the vegetation cover types on Poyang Lake Plain, China. Furthermore, the classification accuracy was validated via field investigations, and its ability to identify the wet season of the area was verified via the annual vague water area changes of Poyang Lake. The results indicate that, compared with the spatial object model based on FSs, the proposed method increases the ability to measure membership error and express spatiotemporal vagueness.