Proposed Clustering Algorithm Research Articles

Clustering analysis for Pythagorean fuzzy sets and its application in multiple attribute decision making

Pythagorean fuzzy sets, as a generalization of intuitionistic fuzzy sets, have a wide range of applications in many fields including image recognition, data mining, decision making, etc. However, there is little research on clustering algorithms of Pythagorean fuzzy sets. In this paper, a novel clustering idea under Pythagorean fuzzy environment is presented. Firstly, the concept of feature vector of Pythagorean fuzzy number (PFN) is presented by taking into account five parameters of PFN, and some new methods to compute the similarity measure of PFNs by applying the feature vector are proposed. Furthermore, a fuzzy similarity matrix by utilizing similarity measure of PFNs is established. Later, the fuzzy similarity matrix is transformed into a fuzzy equivalent matrix which is utilized to establish a novel Pythagorean fuzzy clustering algorithm. Based on the proposed clustering algorithm, a novel multiple attribute decision making (MADM) method under Pythagorean fuzzy environment is presented. To illustrate the effectiveness and feasibility of the proposed technique, an application example is offered.

Trajectory optimization for maximization of energy efficiency with dynamic cluster and wireless power for UAV‐assisted maritime communication

AbstractNowadays, the digital development of marine ranching requires a communication system with wide coverage, high transmission rate and stable communication links. It is known that fixed‐wing unmanned aerial vehicles (UAVs) have great advantages in long‐range applications. They have the potential to serve as low‐altitude communication platforms for maritime communication. In this study, with a developmental perspective, considering the intense growth of marine terminals in the future, a new clustering algorithm applied to cluster nonorthogonal multiple access (C‐NOMA) is proposed and its advantages are investigated. In addition, considering the limited energy of marine terminals, combining the wireless power communication (WPC) technology for the UAV to charge terminals, the charging and communication time are optimized with the Lagrange multiplier method and the bisection search method. After completing the above optimization content of charging and communication, combined with the optimization results, it is found the trajectory that maximizes the energy efficiency of the UAV with the convex optimization technique. Experimental results show that the proposed clustering algorithm has good throughput performance, better fairness and lower algorithm complexity, and the proposed trajectory optimization scheme has better energy efficiency.

Customer analysis using a deep inferarer classifier and a variable-sensitive clustering algorithm optimized by the Cuckoo search method

In this paper, we proposed classification and clustering algorithms that are proper for analyzing customer-related datasets, which are mostly high-dimensional with too many instances. For the clustering purpose, This paper presents a Cuckoo-Search-based Variable Weighting (CSVW) Clustering algorithm to obtain optimal variable weights of high-dimensional data for each cluster. This paper also proposes a deep Inferarer Classifier for categorizing customers using Bi-Directional Long Short-Term Memory (Bi-LSTM) neural network, which uses a Fuzzy Inferential Classifier on its last layer. The Insurance Company (TIC) and InstaCart datasets are utilized for the experiments and performance evaluation. Simulation results reveal that the proposed clustering algorithm generates appropriate Silhouette and Elbow criteria scores in a few cycles of execution in comparison to ordinal clustering algorithms. Also, the proposed classification algorithm with fuzzy soft-max classifier hits the better Classification Criteria in comparison.

Clustering-Based Multi-Region Coverage-Path Planning of Heterogeneous UAVs

Unmanned aerial vehicles (UAVs) multi-area coverage-path planning has a broad range of applications in agricultural mapping and military reconnaissance. Compared to homogeneous UAVs, heterogeneous UAVs have higher application value due to their superior flexibility and efficiency. Nevertheless, variations in performance parameters among heterogeneous UAVs can significantly amplify computational complexity, posing challenges to solving the multi-region coverage path-planning problem. Consequently, this study studies a clustering-based method to tackle the multi-region coverage path-planning problem of heterogeneous UAVs. First, the constraints necessary during the planning process are analyzed, and a planning formula based on an integer linear programming model is established. Subsequently, this problem is decomposed into regional allocation and visiting order optimization subproblems. This study proposes a novel clustering algorithm that utilizes centroid iteration and spatiotemporal similarity to allocate regions and adopts the nearest-to-end policy to optimize the visiting order. Additionally, a distance-based bilateral shortest-selection strategy is proposed to generate region-scanning trajectories, which serve as trajectory references for real flight. Simulation results in this study prove the effective performance of the proposed clustering algorithm and region-scanning strategy.

Subnetworks in BlockDAG

In this article, we study the horizontal scaling of the Waterfall network or similar blockDAG networks by applying hierarchical and graph-based clustering algorithms to partition them into subnetworks. This results in a reduction of network load and an increase in potential performance parameters of the underlying protocol. We consider methods of topology construction, propose clustering algorithms, and perform a simulation of the partitioning of a network into subnetworks.

Demand-side adjustable resources unified data model study

With the large-scale grid integration of new energy sources, the power relationship between source and demand side is becoming more and more complex, the flexibility requirements of the distribution network are increasing, and new requirements for system safety and reliability are put forward, and it can’t meet the peak regulating requirements of grid by relying solely on source side to cope with variable loads. In view of this, to make the grid operation flexibility improved by using an adjustable load capacity, the study first constructs a load classifying method on the foundation of fuzzy style K-plane clustering method to understand the interaction information of the load devices. Then the adjustable value of the load is analyzed from the demand response and standby perspectives, respectively, and an improved dynamic time-bending-based source-load similarity inscription method is proposed, which aims to unify the multiple load information obtained by clustering. The proposed clustering algorithm takes the highest value of 0.096 for the Davies-Bouldin index index, which is 0.012 and 0.014 higher than the K-plane clustering algorithm and the K-means algorithm, respectively. In addition, the load demand response regulation with the improved dynamic time bending method has a higher capacity for new energy consumption than the variance method, with a difference of 2.0 kW. This indicates that using load regulation to consume new energy to exploit the load curve will stimulate the active participation of customer-side load in maintaining power balance between the electricity consumption side and the demand side, and form an interest community between power supplying and demanding sides.

Fuzzy C-means clustering with hesitant fuzzy linguistic preference relation

With the advancement of technology and growing social demand, large scale group decision making has gained significant importance in the field of decision making. Clustering analysis plays a crucial role in enhancing the efficiency of large scale group decision making processes. Linguistic evaluation is more in line with people’s cognitive and expressive habits. The hesitant fuzzy linguistic term set (HFLTS) offers more flexibility in expressing evaluation information. This paper is dedicated to designing a fuzzy C-means clustering model that is specifically applicable for the hesitant fuzzy linguistic preference relation (HFLPR). The objective function of the model is built based on the dissimilarity between HFLPRs and the initial cluster centers to obtain the fuzzy membership matrix and cluster centers. Since initializing cluster centers is a crucial step to produce a reasonable cluster result, three methods are proposed for generating initial centers for HFLPRs. The first and second methods are improvements over existing approaches that dealt with the clustering problem with numerical values. The third approach considers both the preference relation of preferring an alternative and the distribution of the actual preference relations. Based on this, a fuzzy C-means clustering algorithm with HFLPR is designed to obtain the cluster centers and membership matrix for there types of initializing clustering centers. Finally, based on the quality and speed of clustering, a numerical example and comparative analyses illustrate that the proposed clustering algorithm is efficient and effective.

Time series clustering based on normal cloud model and complex network

When data mining research is conducted, it is difficult to obtain precise domain knowledge to set a similarity threshold. Furthermore, noise and missing values are inevitable. Missing values and noise without pre-processing are challenges for many algorithms. A time-series clustering method is proposed based on the normal cloud model and complex networks. Matrix profile similarity measurement, normal cloud model generation and filtering, cloud model expectation curve weighting, degree centrality reweighting, and community discovery in complex networks are the five stages of the proposed clustering algorithm. Local features are considered, and the effects of missing values are reduced when performing similarity measurements. The normal cloud model can be used to set thresholds adaptively. The Louvain algorithm accomplishes the clustering task in complex networks without specifying the clusters. Experiments are conducted on 94 datasets and are compared with 8 clustering methods in the UCR time-series clustering benchmark study. Experimental results indicate that the proposed method can perform well on many datasets.

A Two-Nearest Wireless Access Point-Based Fingerprint Clustering Algorithm for Improved Indoor Wireless Localization

Fingerprint database clustering is one of the methods used to reduce localization time and improve localization accuracy in a fingerprint-based localization system. However, optimal selection of initial hyperparameters, higher computation complexity, and interpretation difficulty are among the performance-limiting factors of these clustering algorithms. This paper aims to improve localization time and accuracy by proposing a clustering algorithm that is extremely efficient and accurate at clustering fingerprint databases without requiring the selection of optimal initial hyperparameters, is computationally light, and is easily interpreted. The two closest wireless access points (APs) to the reference location where the fingerprint is generated, as well as the labels of the two APs in vector form, are used by the proposed algorithm to cluster fingerprints. The simulation result shows that the proposed clustering algorithm has a localization time that is at least 45% faster and a localization accuracy that is at least 25% higher than the k-means, fuzzy c-means, and lightweight maximum received signal strength clustering algorithms. The findings of this paper further demonstrate the real-time applicability of the proposed clustering algorithm in the context of indoor wireless localization, as low localization time and higher localization accuracy are the main objectives of any localization system. Doi: 10.28991/ESJ-2023-07-05-019 Full Text: PDF

Identifying rural high energy intensity residential buildings using metered data

The residential housing stock in rural areas of the U.S. is often older and less energy-efficient than homes built in urban areas. To characterize the energy performance of these buildings and facilitate their energy efficiency upgrades, this study provides a framework for analyzing building energy time-series data to identify buildings with similar temporal energy performance patterns. Using an unsupervised hierarchical time-series clustering algorithm, this framework aims to identify highly inefficient buildings as strong candidates for energy efficiency improvement programs. Weather-normalized electricity and natural gas consumption data from 320 properties in the rural town of Bloomfield, Iowa, covering a six-year reporting period from 2014 to 2019 is used to optimally cluster energy consumption data into two groups, including average and high intensity energy users. To correct the underlying bias of the commonly used area-normalized energy use intensity (EUI) indexes towards smaller homes, the proposed clustering algorithm only compares the EUI of homes with similar floor areas. User types are defined for electricity and natural gas consumption separately to allow for tailored actions and policy recommendations. Accordingly, 5% and 7% of residences in the dataset were classified as high intensity users for electricity and natural gas, respectively. Of the remaining, 88% of homes were classified as average intensity users for both energy types. Only 2 properties in the dataset (less than 1%) were identified to be high intensity users for both electricity and natural gas. Homes classified as high intensity electricity and natural gas users, although smaller in number, were, on average, 44% and 30% more energy intensive, respectively, when compared to similar-sized average intensity consumers. The results also indicated that older homes are generally more likely to be high intensity natural gas consumers while a relatively higher percentage of newer homes were classified as high intensity electricity users.

Targeted demand response for flexible energy communities using clustering techniques

The present study proposes clustering techniques for designing demand response (DR) programs targeting commercial and residential prosumers. The goal is to alter the consumption behavior of the prosumers within a distributed energy community in Italy. This aggregation aims to: (a) minimize the reverse power flow at the primary substation, occurring when generation from solar panels in the local grid exceeds consumption, and (b) shift the system wide peak demand, that typically occurs during late afternoon. Regarding the clustering stage, we consider daily prosumer load profiles and divide them across the extracted clusters. Three popular machine learning algorithms are employed, namely k-means, k-medoids and agglomerative clustering. We evaluate the methods using multiple metrics including a novel metric proposed within this study, namely peak performance score (PPS). The k-means algorithm with dynamic time warping distance considering 14 clusters exhibits the highest performance with a PPS of 0.689. Subsequently, we analyze each extracted cluster with respect to load shape, entropy, and load types. These characteristics are used to distinguish the clusters that have the potential to serve the optimization objectives by matching them to proper DR schemes including time of use, critical peak pricing, and real-time pricing. Our results confirm the effectiveness of the proposed clustering algorithm in generating meaningful flexibility clusters, while the derived DR pricing policy encourages consumption during off-peak hours. The developed methodology is robust to the low availability and quality of training datasets and can be used by aggregator companies for segmenting energy communities and developing personalized DR policies.

A spectral algorithm for quasi-regional geotechnical site clustering

Geotechnical site clustering refers to identifying (or learning) groups in a site-labelled soil/rock database based on inter-site similarity. It is common to cluster sites in a “region” in practice, be it a geographical area or a geologic zone. Data-driven clustering may produce solutions outside these conventional geographical/geologic demarcations. Hence, it is termed “quasi-regional” clustering. This study presents a spectral algorithm for quasi-regional clustering based on a recently proposed hierarchical Bayesian site similarity measure (HBSSM). The HBSSM is utilized to construct the inter-site similarity matrix of the site-labelled database. Quasi-regional clustering is thereafter performed based on the eigenvectors of a transformed similarity matrix. Using numerical and real examples, it is shown that the presented clustering algorithm can produce reasonable clustering results. The proposed clustering algorithm is applicable to geotechnical sites with Multivariate, Uncertain and Unique, Sparse and InComplete (MUSIC) data. It only considers similarity in means, variances, and cross-correlations. The similarity in the spatial variation (e.g., with respect to depth) is not considered.

A Proposed Clustering Algorithm for Efficient Clustering of High-Dimensional Data

To partition transaction data values, clustering algorithms are used. To analyse the relationships between transactions, similarity measures are utilized. Similarity models based on vectors perform well with low-dimensional data. High-dimensional data values are clustered using subspace clustering techniques. Clustering high-dimensional data is difficult due to the curse of dimensionality. Projective clustering seeks out projected clusters in subsets of a data space's dimensions. In high-dimensional data space, a probability model represents predicted clusters. A model-based fuzzy projection clustering method to find clusters with overlapping boundaries in different projection subspaces. The system employs the Model Based Projective Clustering (MPC) method. To cluster high-dimensional data, projective clustering algorithms are used. A subspace clustering technique is the model-based projective clustering algorithm. Similarity analysis use non-axis-subspaces. Anomaly transactions are segmented using projected clusters. The suggested system is intended to cluster objects in high-dimensional spaces. The similarity analysis includes non-access subspaces. The clustering procedure validates anomaly data values with similarity. The subspace selection procedure has been optimized. A subspace clustering approach is the model-based projective clustering algorithm. Similarity analysis use non-axis-subspaces. Anomaly transactions are segmented using projected clusters. The suggested system is intended to cluster objects in high-dimensional spaces. The similarity analysis includes non-access subspaces. The clustering procedure validates anomaly data values with similarity. The subspace selection procedure has been improved.

Combined Gaussian Mixture Model and Pathfinder Algorithm for Data Clustering.

Data clustering is one of the most influential branches of machine learning and data analysis, and Gaussian Mixture Models (GMMs) are frequently adopted in data clustering due to their ease of implementation. However, there are certain limitations to this approach that need to be acknowledged. GMMs need to determine the cluster numbers manually, and they may fail to extract the information within the dataset during initialization. To address these issues, a new clustering algorithm called PFA-GMM has been proposed. PFA-GMM is based on GMMs and the Pathfinder algorithm (PFA), and it aims to overcome the shortcomings of GMMs. The algorithm automatically determines the optimal number of clusters based on the dataset. Subsequently, PFA-GMM considers the clustering problem as a global optimization problem for getting trapped in local convergence during initialization. Finally, we conducted a comparative study of our proposed clustering algorithm against other well-known clustering algorithms using both synthetic and real-world datasets. The results of our experiments indicate that PFA-GMM outperformed the competing approaches.

Dynamic conditional score model-based weighted incremental fuzzy clustering of consumer power load data

This study proposes a weighted incremental fuzzy C-mean power load clustering algorithm based on the dynamic conditional score model to solve the problems that the predominant power load data mining method only captures the mean characteristics of time series and ignores the heteroscedasticity and sequence correlations. Consequently, this method has unsatisfactory time series clustering and low clustering accuracy. A dynamic conditional score model is constructed to analyze and extract statistical characteristic parameters of a time series to calculate the autocorrelation value of the parameter series. A weighted fuzzy C-mean clustering analysis is performed, and the obtained data weight information is used as input for incremental clustering to improve the clustering accuracy. The DCS model parameter dataset and data weight information are combined, and the clustering analysis of the consumer power load data stream is performed. The power load time series of different companies is given, and the clustering validity indices are defined for the performance analysis to verify the proposed clustering algorithm. The experimental results show that the proposed algorithm achieves satisfactory clustering and improves the performance.

Enhancing Data Security in IoT Networks with Blockchain-Based Management and Adaptive Clustering Techniques

The rapid proliferation of smart devices in Internet of Things (IoT) networks has amplified the security challenges associated with device communications. To address these challenges in 5G-enabled IoT networks, this paper proposes a multi-level blockchain security architecture that simplifies implementation while bolstering network security. The architecture leverages an adaptive clustering approach based on Evolutionary Adaptive Swarm Intelligent Sparrow Search (EASISS) for efficient organization of heterogeneous IoT networks. Cluster heads (CH) are selected to manage local authentication and permissions, reducing overhead and latency by minimizing communication distances between CHs and IoT devices. To implement network changes such as node addition, relocation, and deletion, the Network Efficient Whale Optimization (NEWO) algorithm is employed. A localized private blockchain structure facilitates communication between CHs and base stations, providing an authentication mechanism that enhances security and trustworthiness. Simulation results demonstrate the effectiveness of the proposed clustering algorithm compared to existing methodologies. Overall, the lightweight blockchain approach presented in this study strikes a superior balance between network latency and throughput when compared to conventional global blockchain systems. Further analysis of system under test (SUT) behavior was accomplished by running many benchmark rounds at varying transaction sending speeds. Maximum, median, and lowest transaction delays and throughput were measured by generating 1000 transactions for each benchmark. Transactions per second (TPS) rates varied between 20 and 500. Maximum delay rose when throughput reached 100 TPS, while minimum latency maintained a value below 1 s.

Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

Clustering of independent component (IC) topographies of Electroencephalograms (EEG) is an effective way to find brain-generated IC processes associated with a population of interest, particularly for those cases where event-related potential features are not available. This paper proposes a novel algorithm for the clustering of these IC topographies and compares its results with the most currently used clustering algorithms. In this study, 32-electrode EEG signals were recorded at a sampling rate of 500 Hz for 48 participants. EEG signals were pre-processed and IC topographies computed using the AMICA algorithm. The algorithm implements a hybrid approach where genetic algorithms are used to compute more accurate versions of the centroids and the final clusters after a pre-clustering phase based on spectral clustering. The algorithm automatically selects the optimum number of clusters by using a fitness function that involves local-density along with compactness and separation criteria. Specific internal validation metrics adapted to the use of the absolute correlation coefficient as the similarity measure are defined for the benchmarking process. Assessed results across different ICA decompositions and groups of subjects show that the proposed clustering algorithm significantly outperforms the (baseline) clustering algorithms provided by the software EEGLAB, including CORRMAP.

Coordinated Scheduling for Multimicrogrid Systems Considering Mobile Energy Storage Characteristics of Electric Vehicles

Because of the rapid development of electric vehicles (EVs), the energy management of multimicrogrid (MMG) systems has attracted considerable research attention. The objective of this study is to coordinate scheduling performance for MMG systems under large-scale EV operations. To address the problem that the calculation time increases exponentially with the scale of EVs, a clustering algorithm was proposed to speed up the solving efficiency of the coordinated scheduling of MMG systems. To address the problem of the departure and arrival times of clustering EV crossover among various MGs, the proposed clustering algorithm set the boundary of the parking period of clustering EVs by maximizing their controllable time in various MGs. The coordinated scheduling strategy of MMG systems was executed considering the charging cost of EVs, the optimization of transmission power curves, and the absorption of renewable energy. The simulation results revealed that by fully utilizing the mobile energy storage characteristics of EVs, the performance of MMG systems can be maximized. Meanwhile, the computing efficiency of coordinated scheduling can be considerably improved in the case of large-scale EVs integrated into MMG systems by using the proposed clustering algorithm.

Hierarchical clustering with spatial adjacency constraints in heavy-tailed areal data

Some natural phenomena with areal/lattice data structures include extreme values or outliers. In such situations, the assumption of Gaussianity for the random field may not be reasonable, and no Gaussian transformation can be found because they exhibit heavy tails. A non-Gaussian stable random field, which is heavy-tailed, could be a more appropriate choice in these cases. This article introduces a sub-Gaussian α-stable (SGαS) random field for spatial analysis of multivariate areal data using a multivariate conditional autoregressive model. We, specifically, focus on the spatial clustering problem of such areal data. To address it, we develop methods that work based on adjacency constraints. To group the data, we offer an adjacency-constrained hierarchical agglomerative clustering (HAC) technique that considers both spatial and non-spatial attributes existing in the multivariate areal data. The proposed clustering algorithm is developed based on spatial adjacency constraint criteria incorporated into the HAC technique. We employed the developed algorithm to group Luxembourg communes based on simulated areal data from the SGαS distributions and French cities along the Gironde estuary based on the estuary areal dataset. We compare the results of the proposed method with the mentioned adjacency criteria, various dissimilarity, and linkages measures in clustering these two datasets.

Millimeter Channel Clustering by Self-Organizing Maps With Time-Varying Topological Structure

Cluster-based channel model plays an important role in the fifth-generation (5G) and beyond 5G (B5G) wireless communication systems. A well-designed clustering algorithm can achieve a good compromise between the accuracy and complexity of the wireless channel model. The dependence on prior information of cluster, such as the number of clusters, is a common dilemma of current algorithms. In this article, a self-organizing map with time-varying topological structure (SOM-TVS) clustering algorithm is proposed. First, we combine the locations and weights of the neurons in competition layer and initialize the SOM according to multipath components (MPCs), in which the topology of the SOM is changed with the iterations of the algorithm. Then, a new MPCs’ power weighted Gaussian–Sinc function is proposed to optimize the competitiveness performance of the algorithm. Finally, both the measured and simulated channels in millimeter wave (mmWave) are used to validate the performance of the proposed SOM-TVS algorithm. The SOM-TVS algorithm can perform great effectiveness and robustness based on numerical simulations and experimental validation results. More significantly, the proposed clustering algorithm does not require any prior knowledge of cluster and has a fairly low complexity.