Network Clustering Method Research Articles

Forecasting Directions, Dates, And Causes of Future Technological Revolutions concerning the Growth of Human Capital

Technology forecasting is an important and critical issue that determines the starting point of planning and is considered as a management tool directly related to the future. In the previous research items, the development of renewable energy technologies was of concern. Moreover, due to the increasing need of countries to produce electricity and facing the lack of resources, this research focuses on forecasting photovoltaic technology. Accordingly, in this paper, for technological research in the field of solar energy, the patents extracted from one of the most famous renewable energy databases in the United States (US patent database) between 200 and 2020 were examined. Next, research gaps were analyzed by using the artificial neural network clustering method and also by analyzing covered and uncovered compounds. The results show that in the future, photovoltaic technology research will move towards the third generation of technology (organic materials) as well as focus on environmental parameters and their effects on the performance of photovoltaic systems.

Social network clustering and consensus-based distrust behaviors management for large-scale group decision-making with incomplete hesitant fuzzy preference relations

With the development of social network platforms, large-scale group decision-making in social network (LSGDM-SN) has been formed. As decision makers (DMs) come from different fields and have complex individual backgrounds, which leads to their distrust in the moderator. Moreover, in LSGDM-SN, since DMs can hardly grasp all the information about the decision problem, the hesitant fuzzy preference relations (HFPRs) they have expressed may be incomplete. However, in current LSGDM-SN issues, the distrust behaviors and incomplete HFPRs have never been discussed simultaneously. In this context, this paper aims to propose a method to estimate incomplete values in HFPRs, and develop a consensus management process which considers distrust behaviors. This paper focuses on LSGDM-SN on the basis of social network clustering and consensus-based distrust behaviors management with incomplete HFPRs. In this paper, a social network clustering method based on grey clustering algorithm is proposed to classify the DMs with similar social clustering degree into a subset. Afterwards, a method including two situations is developed to estimate incomplete values in HFPRs. Furthermore, an identification mechanism is presented to detect the DMs’ distrust behaviors, and three modification strategies are provided for managing different types of distrust behaviors. In addition, a case study is given to illustrate the feasibility of the proposed method. Finally, comparative analysis and discussion are explored to verify the advantages of the proposed LSGDM-SN with incomplete HFPRs.

Sustaining accurate detection of phishing URLs using SDN and feature selection approaches

Phishing is an online fraud that deceives visitors by impersonating a legitimate website to steal their confidential or personal information. This is a well-known form of cybercrime. With the aim of detecting phishing sites, several phishing site detection techniques have recently been created. However, it fails to achieve the desired goal and has a large number of drawbacks, including low accuracy, long learning curve, and low-power embedded hardware. For covering such drawbacks, this work proposes an efficient URLs Phishing detection technique. Our technique depends on Software Defined Network (SDN) technology, clustering and feature method, and Conventional Neural Network (CNN) algorithm. Feature selection technique is based on Recursive Feature Elimination (RFE) with Support Vector Machine (SVM) algorithm. The SDN is used to transfer the URLs phishing detection process out of the user's hardware to the controller layer, continuously train on new data, and then send its outcomes to the SDN-Switches. RFE-SVM and CNN are used to increase accuracy of phishing detection. Therefore, the proposal model does not require retrieving the content of the target website or using any third-party services. It captures the information and sequential patterns of URL strings without requiring a prior knowledge about phishing, and then uses the sequential pattern features to quickly classify the actual URL. The experimental results showed that our proposal highlighted the robustness and accuracy in distinguishing between phishing and legitimate sites. Our suggestion achieves 99.5% phishing detection accuracy.

Clustering Schemes Use for To Mobile Ad Hoc Network

The MANETs are effectively deployed in the different environments such as: Armed Forces, Disaster Management, Medical and other similar Integrated Applications . Observed that new challenges and applications come up in the MANETs in every short period of a time. In the large deployments MANETs can have huge number of mobile nodes creating new problems. In the order to overcome such challenges, clustering algorithms are a invariably used to allow the structuring of the network Clustering methods allow fast connection and also for better routing and to the topology management of mobile ad hoc networks (MANET). The assortment of approaches for the MANET grouping has been created by scientists which center around various kinds of execution measurements. In this paper presents a analysis of various clustering schemes for the MANET into group of entities or the cluster and proposes efficient approach.

A Security Log Analysis Scheme Using Deep Learning Algorithm for IDSs in Social Network

Due to the complexity of the social network server system, various system abnormalities may occur and in turn will lead to subsequent system failures and information losses. Thus, to monitor the system state and detect the system abnormalities are of great importance. As the system log contains valuable information and records the system operating status and users’ behaviors, log data in system abnormality detection and diagnosis can ensure system availability and reliability. This paper discloses a log analysis method based on deep learning for an intrusion detection system, which includes the following steps: preprocess the acquired logs of different types in the target system; perform log analysis on the preprocessed logs using a clustering-based method; then, encode the parsed log events into digital feature vectors; use LSTM-based neural network and log collect-based clustering methods to learn the encoded logs to form warning information; lastly, trace the source of the warning information to the corresponding component to determine the point of intrusion. The paper finally implements the proposed intrusion detection method in the server system, thereby improving the system’s security status.

Information spillover features in global financial markets: A systematic analysis

This study investigates the characteristics of information spillover in 32 major stock markets worldwide and takes into account the Global Financial Crisis in 2008, using the information spillover test method proposed by Hong and complex network tools. The results provide evidence that risk spillover is the strongest among four kinds of information spillover, and accelerates and aggregates post crisis. Downside extreme risk spillover is stronger than upside extreme risk spillover; the former spreads faster and the latter slows post crisis. We assign markets to four roles: leader, follower, communicator, and independent. We then distinguish the role of each market and information spillover pattern pre and post crisis. We further explain the characteristics of different communities in the global markets based on our proposed network clustering method.

Decision Routing Problems in A Wireless Sensor Network Based on A Neural Mechanism

This article proposes a solution for the routing problem in wireless sensor networks (WSN) based on a neural mechanism. The basic concepts of wireless sensor networks, artificial neural networks (ANNs), and WSN routing protocols are presented. The Kohonen ANN was selected to solve the problem of routing in wireless sensor networks based on a neural mechanism. A radio visibility matrix is proposed, which is a mathematical description of the connectivity of network nodes and the radio visibility of each node with respect to all other network nodes. Based on the Kohonen ANN trained by the constructive method, a method for WSN neural network clustering was developed. Two software-modeling environments are presented that were created to confirm the theory with respect to the logic of the developed methods for WSN clustering, their correction and the verification of their adequacy. Numerical results of modeling the solution of the routing problem in a wireless sensor network based on a neural mechanism by neural network clustering, the WSN matrix clustering method and the energy distance neural clustering protocol (EDNCP) are presented. It was found that the developed EDNCP protocol was 29% more efficient than known analogs.

A medical network clustering method with weighted graph structure

Today, most of the databases used for drug information mining are derived from the collection of many treatments under a single disease, and some special drug compatibility rules can be found from them. However, researchers’ exploration of medical data is not limited to this. The comparative analysis of drugs for different diseases has become a new research point. In this paper, the drug is used as a node, the relationship is the edge connecting the two nodes, the co-occurrence frequency of the drug is used as the weight of the edge to establish a network graph. We use the clustering algorithm of the weighted network graph center diffusion method combining the network topology and the edge weights to divide the network graph into communities. Then we proposed the Structural Clustering Algorithm on Weighted Networks (SCW), it helps to study the prescription of medical prescriptions and provides more scientific recommendations for auxiliary prescriptions. In the experiment, SCW is compared with the classic community discovery algorithm CPM, the network function modular analysis algorithm MCODE and the hierarchical network graph structure analysis algorithm BGLL. We analyze the results according to NMI, ARI and F-Measure. Finally, a case study of real data was conducted to ensure the correctness and effectiveness of the algorithm, and to obtain the potential drug combination in the medical prescription.

Clustering Research in Fifth Generation Mobile Communication Networks

Clustering procedures are utilized to broaden the life of systems and increment vitality proficiency. In this study, some clustering algorithms and traffic expectations consider in 5G communication systems are inspected. There is a need to explain how to improve the nature of client experience through clustering. Understanding the requirements of clients is basic to give the capacity to help various situations in smart frameworks. Client mindfulness or client situated plan is a challenge in clustering. Inquires about have demonstrated that the usage of clustering plans in 5G systems presents difficulties and that clustering procedures created with insightful system determination arrangements can be of extraordinary advantage. The present investigations are not perfect in unique frameworks with a wide assortment of client situations since they are performed in both homogeneous and low-level heterogeneous systems and can't work. Also, when the 5G happens, the issue will turn out to be more mind-boggling than customarily rearranged. Different challenges identified with the execution of clustering methods in 5G networks are introduced and examined.

Clustering for Items Distribution Network

The distribution network can be represented as a simple graph. In this case, the delivery destination location is represented as a vertex, while the road passed is represented as an arc. The main concept of network clustering or graph clustering is that high connected vertices can be in the same cluster, while low connected vertices can be in different clusters. The method for network clustering in this paper is the Markov cluster. The purpose of items distribution network clustering i.e. to facilitate the company in item delivery based on road conditions.

A constrained agglomerative clustering approach for unipartite and bipartite networks with application to credit networks

Researchers and practitioners have been interested in solving real-world problems through clustering. The clustering of nodes in networks with unipartite or bipartite structure is important to explore real-world complex networks present in nature and society. Bipartite networks form an important class of complex networks because they reveal the heterogeneity of nodes in a network. However, most extant clustering methods focus only on unipartite networks. In this work, a novel constrained agglomerative clustering method applicable to unipartite and bipartite networks has been proposed. Initially, the topology of a network is modeled according to set-theoretic principles. Subsequently, the concepts related to rough set theory and relative linkage are used to cluster the set of nodes. The utility and effectiveness of the proposed approach are demonstrated through offline experiments on unipartite and bipartite networks. A comparison against ten state-of-the-art similarity measures over two different partitional clustering algorithms reveals the effectiveness of the proposed relative linkage measure. Moreover, a comparative analysis with state-of-the-art network clustering methods reveals the viability of the proposed rough set-based constrained agglomerative clustering algorithm. Finally, the proposed method has been applied for the detection of cohesive subgroups of banks in a real bipartite network formed by mapping credit relationships between Indian firms and banks.

Hierarchical Clustering of Bipartite Networks Based on Multiobjective Optimization

Complex network modeling is an elegant yet powerful tool to delineate complex systems. Hierarchical clustering of complex networks can readily facilitate our comprehension of the higher order organizations of complex systems. Among all the complex network models, bipartite network is an essential part. In this paper we present a multiobjective optimization based hierarchical clustering algorithm for bipartite networks. In doing so, we first devise a similarity index whereby a bipartite network is mapped into a monopartite network. We further put forward a multiobjective optimization model for monopartite network clustering. Finally we develop an agglomerative method for deriving the hierarchical tree structure of the original bipartite network. To evaluate the effectiveness of our proposed bottom-up hierarchical clustering algorithm, we carry out experiments on ten bipartite ecological networks. We also compare our algorithm with one state-of-the-art bipartite network clustering algorithm and one highly efficient hierarchical network clustering method. Experimental comparisons show the efficiency of our proposed algorithm for hierarchical clustering of bipartite networks. By further analyzing the hierarchical trees derived by our proposed algorithm we find that our obtained trees are biologically appealing and could have potential implications for species classification.

A Distributed Multi-Competitive Clustering Method for Wireless Sensor Networks using 5G LTE

Fifth era remote, or 5G, is the most recent emphasis of cell innovation, designed to extraordinarily build the speed and responsiveness of remote systems. With 5G, information transmitted over remote broadband associations could go at rates as high as 20 Gbps by a few appraisals surpassing wire line organize speeds and in addition offer inactivity of 1 ms or lower for utilizes that require ongoing criticism. 5G will likewise empower a sharp increment in the measure of information transmitted over remote frameworks because of more accessible transfer speed and propelled reception apparatus technology. Wireless sensor systems (WSNs) require basic and successful ways to deal with lessen vitality utilization in view of restricted vitality. Grouping hubs is a compelling way to deal with make WSNs vitality productive. In this paper we propose a dispersed multi-aggressive bunching approach (DMCC) for WSNs. To start with, the hubs with high leftover vitality are chosen to go about as group head hopefuls (CHCs). Second, bunch heads (CHs) are chosen from the CHCs in light of a crossover of rivalry. In the event that the separations to the chose CHs are reasonable, a CHC with more neighbour hubs and littler normal separation to its neighbour hubs will probably turn into a CH. In the event that the quantity of CHs chose from the CHCs is insufficient, more CHs are chosen from non-CHCs constantly as indicated by lingering vitality until the CHs number is reasonable. DMCC makes the CHs number stable and disperse the CHs equally. Recreation tests were performed on to contrast DMCC and some related bunching approaches. The exploratory outcomes recommend that DMCC adjusts the heap among various groups and lessens the vitality utilization, which enhances the system lifetime.

Measurement-Based Network Clustering for Active Distribution Systems

This paper presents a network clustering (NC) method for active distribution networks (ADNs). Following the outage of a section of an ADN, the method identifies and forms an optimum cluster of microgrids within the section. The optimum cluster is determined from a set of candidate microgrid clusters by estimating the following metrics: total power loss, voltage deviations, and minimum load shedding. To compute these metrics, equivalent circuits of the clusters are estimated using measured data provided by phasor measurement units (PMUs). Hence, the proposed NC method determines the optimum microgrid cluster without requiring information about the network’s topology and its components. The proposed method is tested by simulating a study network in a real-time simulator coupled to physical PMUs and a prototype algorithm implementation, also executing in real time.

A New Centralized Clustering Algorithm for Wireless Sensor Networks.

Clustering is presently one of the main routing techniques employed in randomly deployed wireless sensor networks. This paper describes a novel centralized unequal clustering method for wireless sensor networks. The goals of the algorithm are to prolong the network lifetime and increase the reliability of the network while not compromising the data transmission. In the proposed method, the Base Station decides on the cluster heads according to the best scores obtained from a Type-2 Fuzzy system. The input parameters of the fuzzy system are estimated by the base station or gathered from the network with a careful design that reduces the control message exchange. The whole network is controlled by the base station in a rounds-based schedule that alternates rounds when the base station elects cluster heads, with other rounds in which the cluster heads previously elected, gather data from their contributing nodes and forward them to the base station. The setting of the number of rounds in which the Base Station keeps the same set of cluster heads is another contribution of the present paper. The results show significant improvements achieved by the proposal when compared to other current clustering methods.

A review on data clustering using spiking neural network (SNN) models

The evolution of Artificial Neural Network recently gives researchers an interest to explore deep learning evolved by Spiking Neural Network clustering methods. Spiking Neural Network (SNN) models captured neuronal behaviour more precisely than a traditional neural network as it contains the theory of time into their functioning model [1]. The aim of this paper is to reviewed studies that are related to clustering problems employing Spiking Neural Networks models. Even though there are many algorithms used to solve clustering problems, most of the methods are only suitable for static data and fixed windows of time series. Hence, there is a need to analyse complex data type, the potential for improvement is encouraged. Therefore, this paper summarized the significant result obtains by implying SNN models in different clustering approach. Thus, the findings of this paper could demonstrate the purpose of clustering method using SNN for the fellow researchers from various disciplines to discover and understand complex data.

Spectral clustering methods for multiplex networks

Clustering nodes of a network into communities is a basic tool for identifying structure in complex system. We extend spectral clustering to multiplex structures and discuss some of the difficulties that arise in defining natural generalizations of the single layer case. To analyze the effectiveness of our approaches, we construct three simple families of synthetic multiplex networks and compare the performance of two different versions of multiplex spectral clustering. Our two clustering models – one based on the diagonal supra-Laplacian and the other based on a different diffusive model – perform differently on our tests. The first essentially interpolates between splitting the layers of the networks into different clusters and finding the layer clusters while the second interpolates between the layer clusterings, often finding clusters that span the different layers which reflect multiplex characteristics of the network.

Application of Remote Sensing to Identify and Monitor Seasonal and Interannual Changes of Water Turbidity in Yellow River Estuary, China

AbstractWater turbidity is an important indicator for water security and environmental security in the Yellow River estuary. However, due to the complex terrain and harsh climatic environment, it is difficult to monitor the water turbidity over the complex surface of the estuary. In this study we applied a self‐organizing map clustering method, an artificial neural network clustering method, to extract turbidity patterns from the long‐term remote sensing data sets in the Yellow River estuary. Based on the Moderate Resolution Imaging Spectroradiometer data from 2000 to 2015, six turbidity patterns were identified by using the self‐organizing map clustering method: high turbidity pattern, moderate turbidity pattern, low turbidity pattern, very low turbidity pattern, extreme high turbidity pattern and sea ice pattern, and the first four patterns appear every year. All patterns have significant seasonal characteristics, and monthly turbidity is dominated by one of these turbidity patterns. The water turbidity in the estuary has decreased in the past 16 years, and the interannual variation of the turbidity pattern is the result of the combination of the sediment transported into the sea by the Yellow River and the wind and waves on the sea surface.

An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks.

A wireless sensor network (WSN) is an essential component of the Internet of Things (IoTs) for information exchange and communication between ubiquitous smart objects. Clustering techniques are widely applied to improve network performance during the routing phase for WSN. However, existing clustering methods still have some drawbacks such as uneven distribution of cluster heads (CH) and unbalanced energy consumption. Recently, much attention has been paid to intelligent clustering methods based on machine learning to solve the above issues. In this paper, an affinity propagation-based self-adaptive (APSA) clustering method is presented. The advantage of K-medoids, which is a traditional machine learning algorithm, is combined with the affinity propagation (AP) method to achieve more reasonable clustering performance. AP is firstly utilized to determine the number of CHs and to search for the optimal initial cluster centers for K-medoids. Then the modified K-medoids is utilized to form the topology of the network by iteration. The presented method effectively avoids the weakness of the traditional K-medoids in aspects of the homogeneous clustering and convergence rate. Simulation results show that the proposed algorithm outperforms some latest work such as the unequal cluster-based routing scheme for multi-level heterogeneous WSN (UCR-H), the low-energy adaptive clustering hierarchy using affinity propagation (LEACH-AP) algorithm, and the energy degree distance unequal clustering (EDDUCA) algorithm.

Heterogeneous Data Clustering Considering Multiple User-provided Constraints

Clustering on heterogeneous networks which consist of multi-typed objects and links has proved to be a useful technique in many scenarios. Although numerous clustering methods have achieved remarkable success, current clustering methods for heterogeneous networks tend to consider only internal information of the dataset. In order to utilize background domain knowledge, we propose a general framework for clustering heterogeneous data considering multiple user-provided constrains. Specifically, we summarize that three types of manual constraints on the object can be used to guide the clustering process. Then we propose the User- HeteClus algorithm to solve the key issues in the case of star-structure heterogeneous data, which incorporating the user constraint into similarity measurement between central objects. Experiments on a real-world dataset show the effectiveness of the proposed algorithm.