Hybrid Clustering Technique Research Articles

HCDQN-ORA: a novel hybrid clustering and deep Q-network technique for dynamic user location-based optimal resource allocation in a fog environment

HCDQN-ORA: a novel hybrid clustering and deep Q-network technique for dynamic user location-based optimal resource allocation in a fog environment

Urban Mobility and Knowledge Extraction from Chaotic Time Series Data: A Comparative Analysis for Uncovering COVID-19 Effects

The COVID-19 pandemic exerted devastating effects on the global economy, public health, and urban life. The geographical consequences lie in spatial time series dimensions, but the chaotic characteristics hinder intuitive understanding. Aiming to discover the variance of urban mobility in the pandemic context, this article presents a hybrid clustering technique called whole time sequence mixture (WTSM). The combination of whole time and subsequence techniques ensures robustness to data volume, dimensionality, sampling, distortion tasks, and a prior constraint. For yellow taxi trips in New York City, the case study spanned the pre-, mid-, and postpandemic periods and compared the performance of the established methods (symbolic aggregate approximation and dynamic time warping) and WTSM techniques. Findings revealed COVID-19 trends and social restriction-induced mobility variations and determined that vaccine supply did not lead to immediate mobility restoration. Meanwhile, concepts of validation indexes and computational complexity corroborate the superiority of WTSM. The transient cluster arising from temporal dissimilarity is a unique finding of WTSM, which led to high cluster cohesion and separation. WTSM can obtain new knowledge and rationale for urgent government intervention and epidemic management, not sacrificing computational efficiency and clustering quality. With the improved storage capability and desire for multidimensional pattern extraction, the increased accessibility to the chaotic time series data sets could promote further studies and help administrative schemes.

An Energy-Efficient Hybrid Clustering Technique (EEHCT) for IoT-Based Multilevel Heterogeneous Wireless Sensor Networks

Internet-of-Things (IoT)-based Heterogeneous Wireless Sensor Network (HWSN) has emerged as a prevalent technology that plays a significant role in developing various human-centric applications. Like in a wireless sensor network (WSN), energy is also the most crucial resource in IoT-based HWSN. The researchers have proposed many works to achieve energy-efficient network operations by minimizing energy usage. A vast proportion of these works emphasize using the clustering approach, which has proved its worth to a great extent. However, most schemes require the repeated formation of clusters incurring a significant amount of nodes’ energy in the clustering process. The protocol design of such schemes also varies with the changing levels of heterogeneity. In this work, a hybrid clustering scheme- An Energy-Efficient Hybrid Clustering Technique (EEHCT) has been proposed for IoT-based HWSN that minimizes the energy consumption in clusters’ formation and distributes the network load evenly irrespective of the heterogeneity level to prolong network lifetime. It appropriately utilizes dynamic and static clustering strategies to formulate the load-balanced clusters in the network. EEHCT establishes its supremacy over state-of-the-art schemes via an extensive set of simulations and experimentation in terms of multiple network performance metrics like stability, throughput, and network lifetime. Like, it achieves a gain up to 90.27% with respect to network lifetime over its peers in the standard operating conditions and under varying network configurations. In addition to quantitative analysis, a statistical analysis has also been provided to demonstrate the formation of energy-balanced clusters through the proposed scheme.

Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network

Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that can be used in VANETs. The problems with the K-Means algorithm concern the selection of a suitable number of clusters, the creation of a highly reliable cluster, and achieving high similarity within a cluster. To address these problems, a novel method combining a covering rough set and a K-Means clustering algorithm (RK-Means) was proposed in this paper. Firstly, RK-Means creates multi-groups of vehicles using a covering rough set based on effective parameters. Secondly, the K-value-calculating algorithm computes the optimal number of clusters. Finally, the classical K-Means algorithm is applied to create the vehicle clusters for each covering rough set group. The datasets used in this work were imported from Simulation of Urban Mobility (SUMO), representing two highway scenarios, high-density and low-density. Four evaluation indexes, namely, the root mean square error (RMSE), silhouette coefficient (SC), Davies–Bouldin (DB) index, and Dunn index (DI), were used directly to test and evaluate the results of the clustering. The evaluation process was implemented on RK-Means, K-Means++, and OK-Means models. The result of the compression showed that RK-Means had high cluster similarity, greater reliability, and error reductions of 32.5% and 24.2% compared with OK-Means and K-Means++, respectively.

COMPARITIVE ANALYSIS OF K++GKFCM CLUSTERING FOR BRAIN TUMOUR DETECTION

Due to the complicated structure of brain tumors, hazy borders, and outside variables like noise, inferring tumor and edema regions from brain magnetic resonance imaging (MRI) data is still difficult. In this paper, a powerful hybrid clustering technique together with morphological procedures is suggested for segmenting brain tumors in order to reduce noise sensitivity and enhance segmentation stability. The following are the paper's key contributions: initially, adaptive Wiener filtering is employed for de-noising, and morphological procedures are applied for deleting non-brain tissue, thereby minimizing the method’s susceptibility to noise. Second, to segment pictures, the fuzzy C-means technique based on a Gaussian kernel is used with K-means++ clustering. This clustering decreases the sensitivity of the clustering parameters while simultaneously enhancing the stability of the algorithm. To produce accurate representations of brain tumours, the retrieved tumour pictures are lastly post processed utilising morphological procedures and median filtering. The suggested approach was also contrasted with other existing segmentation algorithms.

Vampire Attack Mitigation and Network Performance Improvement Using Probabilistic Fuzzy Chain Set with Authentication Routing Protocol and Hybrid Clustering-Based Optimization in Wireless Sensor Network

The most effective threat for wireless sensor networks (WSN) is Vampire attacks on sensor nodes as they can stretch the network connectivity among them and influence the network’s energy, which can drain the network. Vampire attack has particular malicious nature of sensor nodes in which they can widely exploit features of combined routing protocol. Fuzzy rules and fuzzy sets are highly optimal techniques in mitigating the vampire attacks of the network, which can quantify the uncertain behaviour of sensor nodes. This study aims to propose a novel technique using a probabilistic fuzzy chain set with authentication-based routing protocol and hybrid clustering technique for data optimization of the network. The suggested approach here employs a fuzzy-based chain rule set to combat growing types of vampire assaults using probability formulas. The authentication routing protocol has increased network routing security. The proposed technique (PFCS-ARP_HC) has optimized the energy consumption of network. Simulation for this technique has been carried out using NS2 and experimental results show the performance of the proposed model in terms of throughput of 98%, packet delivery ratio of 89%, energy consumption of 67%, latency of 46% control overhead of 53%, and attack detection ratio of 87.9%.

NICASN: Non-negative Matrix Factorization and Independent Component Analysis for Clustering Social Networks

Discovering clusters in social networks is of fundamental and practical interest. This paper presents a novel clustering strategy for large-scale highly-connected social networks. We propose a new hybrid clustering technique based on non-negative matrix factorization and independent component analysis for finding complex relationships among users of a huge social network. We extract the important features of the network and thenperform clustering on independent and important components of the network. Moreover, we introduce a new k-means centroid initialization method by which we achieve higher efficiency. We apply our approach on four well-known social networks: Facebook, Twitter, Academia and Youtube. We experimentally show that our approach achieves much better results in terms of the Silhouette coefficient compared to well-known counterpartssuch as Hierarchical Louvain, Multiple Local Community detection, and k-means++

Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud

Abstract The correct individual tree segmentation of the forest is necessary for extracting the additional information of trees, such as tree height, crown width, and other tree parameters. With the development of LiDAR technology, the research method of individual tree segmentation based on point cloud data has become a focus of the research community. In this work, the research area is located in an underground coal mine in Shenmu City, Shaanxi Province, China. Vegetation information with and without leaves in this coal mining area are obtained with airborne LiDAR to conduct the research. In this study, we propose hybrid clustering technique by combining DBSCAN and K-means for segmenting individual trees based on airborne LiDAR point cloud data. First, the point cloud data are processed for denoising and filtering. Then, the pre-processed data are projected to the XOY plane for DBSCAN clustering. The number and coordinates of clustering centers are obtained, which are used as an input for K-means clustering algorithm. Finally, the results of individual tree segmentation of the forest in the mining area are obtained. The simulation results and analysis show that the new method proposed in this paper outperforms other methods in forest segmentation in mining area. This provides effective technical support and data reference for the study of forest in mining areas.

A Hybrid Clustering Technique to Propose the Countries for HELP International

A Hybrid Clustering Technique to Propose the Countries for HELP International

Predicting and 3D modeling of karst zones using seismic facies analysis in Ordovician carbonates of the Tahe oilfield, China

Karsted carbonates of the Ordovician Yingshan Formation represent significant hydrocarbon reservoirs in the Tarim Basin, China. Due to the geologic complexity of the formation, realistically predicting and modeling karst zones and rock properties is challenging. This drives the need to apply diverse techniques for building a suitable geologic model. We have developed a static model approach that uses fully automated seismic facies classification processes for predicting and modeling patterns associated with karst elements. Our method uses a seismic attribute and well logs as input data. We initially processed a seismic facies volume using the hierarchical clustering technique. This is based on seismic attribute values that take into account an optimal number of classes. The outcome reveals various patterns illustrated with low amplitudes highlighting the geomorphology of paleokarst elements. Simultaneously, a seismic traces map of the karsted interval was processed using the hybrid clustering technique conducted on seismic trace shape. In this case, the karst facies was extracted from the output and used as secondary input data in trend analysis of the model. Both outputs obtained from clustering techniques are processed in a volume of the most probable facies, which delineate the karst patterns. The results of the modeling process are visualized in various time slices and cross sections, appropriately recognizing the relationship of estimated patterns with karst zones. We have evaluated the karstification thickness and porosity map obtained from the 3D model that detail a reasonable connectivity between karst elements. This is based on the paleogeographic location and type of filling, as well as the dissolution development along the main striking faults. Finally, our method outputs a logical model of karst zones located within the host rock, which reduces the uncertainty and identify nonperforated segments.

An Enhanced Hybrid Routing and Clustering Technique for Wireless Sensor Network

An Enhanced Hybrid Routing and Clustering Technique for Wireless Sensor Network

An Enhanced Hybrid Routing and Clustering Technique for Wireless Sensor Network

Wireless Sensor Networks (WSN) have extensively deployed in a wide range of applications. However, WSN still faces several limitations in processing capabilities, memory, and power supply of sensor nodes. It is required to extend the lifetime of WSN. Mainly this is achieved by routing protocols choosing the best transmission path in-network with desired power conservation.This cause is developing a generic protocol framework for WSNa big challenge. This work proposed a new routing technique, described as Hybrid Routing-Clustering (HRC) model. This new approach takes advantage of clustering and routing procedures defined in K-Mean clustering and AODV routing, which constituted of three phases. This development aims to achieve enhanced power conservation rate in consequence network lifetime. An extensive evaluation methodology utilized to measure the performance of the proposed model in simulated scenarios.The results categorized in terms of the average amount of packet received and power conservation rate. The Hybrid Routing-Clustering (HRC) model was determined, showed enhanced results regarding both parameters. In the end, they are comparing these results with well-known routing and well-known clustering algorithms.

Brain Tumor Segmentation and Classification using Hybrid clustering technique and SVM Classifier

Brain tumor Detection is a primary concern in today’s life. So a computer aided technology must be implemented for an accurate detection and identification of brain tumor. The tumor can be detected using various classification techniques from brain MR Images. In this paper segmentation process is being done using K means Clustering technique and Binary Thresholding, the features from the images are then extracted using GLCM where six texture features are extracted and SVM Classifier is being used for classification of the images. This proposed method shows an accuracy of 97.12%.

Stage Classification of Alzheimer‟s disease in MR Brain Image using Hybrid Clustering Technique and Ensemble Classifier

Alzheimer’s Disease (AD) can be identified in the structural variation of MR brain image. Accurate segmentation of brain subjects is an essential requirement to the quantitative analysis for the pre-surgical planning. Brain network takes place a vital role in the staging of AD. Cortical thickness, hippocampus volume and the volume reduction in Grey Matter (GM) have been taken so far in the discrimination of AD subjects from the normal control. The segmentation task has been performed for grouping various brain tissues using Modified Fuzzy C Means algorithm (MFCM) with Particle Swarm Optimization (PSO) and this optimization technique very useful to enhance the segmentation accuracy. Volumetric measure of all the segmented brain tissues have been preferred as one of the feature as well as some structural features also have obtained using Grey Level Co-occurrence Matrix (GLCM) to improve the performance of classifier. In this study, ensemble of classifier has been implemented to classify the severity of the disease. An ensemble of classifier works depends upon the majority voting principle and the error rate of classifier is reduced when compared to the base classifier. The performance of proposed classifier has compared with the base classifiers and verified that the accuracy is improved.

Performance Evaluation of a Fuzzy Hybrid Clustering Technique to Identify Flood Source Areas

Prioritization of flood source areas (FSAs) is of paramount importance in flood management to adopt proportional measures within a watershed. Unit Flood Response (UFR) approach has been proposed to identify FSAs at subwatershed and/or cell scale. In this study, a distributed modified Clark (ModClark) model coupled with Muskingum flow routing method was used for hydrological simulations. Furthermore, SOMFCM clustering techniques involving Self-Organizing Feature Maps (SOFM) and Fuzzy C-Means algorithm (FCM) were used to identify Hydrologic Homogenous Regions (HHRs). The case studies were two semi-arid watersheds including Tangrah in northeastern Iran and eastern part of Walnut Gulch Experimental Watershed (WGEW) in Arizona. DEM-derived geomorphological and hydrological features were entered into Factor Analysis (FA) to determine the most effective variables in runoff generation. The optimum SOMFCM resulted in clustered HHRs map which was generally similar to that of the UFR-delineated FSAs at cell scale, especially in cases of maximum flood index values for both watersheds. Although clustering techniques, such as SOMFCM, cannot directly provide a map of FSAs involving absolute values of flood index, most dominant watershed physical features may be used to identify the most critical, or effective FSAs through clustered HHRs. Application of SOMFCM in two semi-arid watersheds demonstrated that SOMFCM provides a simple and useful tool to qualitatively identify the ranking of FSAs across a watershed. Therefore, the clustered HHRs involving higher ranks of FSAs that represent the most flood active regions, are expected to assist policymakers for effective management of floods.

K-Means Clustering in WSN with Koheneon SOM and Conscience Function

Wireless Sensor Networks (WSNs) are broadly utilized in the recent years to monitor dynamic environments which vary in a rapid way over time. The most used technique is the clustering one, such as Kohenon Self Organizing Map (KSOM) and K means. This paper introduces a hybrid clustering technique that represents the use of K means clustering algorithm with the KSOM with conscience function of Neural Networks and applies it on a certain WSN in order to measure and evaluate its performance in terms of both energy and lifetime criteria. The application of this algorithm in a WSN is performed using the MATLAB software program. Results demonstrate that the application of K-means clustering algorithm with KSOM algorithm enhanced the performance of the WSN which depends on using KSOM algorithm only in which it offers an enhancement of 11.11% and 3.33% in terms of network average lifetime and consumed energy, respectively. The comparison among the current work and a previous one demonstrated the effectiveness of the proposed approach in this work in terms of reducing the energy consumption.

A fast hybrid clustering technique based on local nearest neighbor using minimum spanning tree

With rapid explosion of information, clustering emerged as an active research area for knowledge discovery. Most of the existing clustering algorithms become ineffective when inappropriate parameters are provided or applied on a dataset which consists of clusters of diverse shapes, sizes, and varying densities. To overcome these issues, many graph based hybrid clustering algorithms have been proposed but these algorithms first generate a complete graph of the dataset which takes O(N2) time where N is the number of data points which limits their application on large datasets. This paper proposes an algorithm namely a fast hybrid clustering technique based on local nearest neighbor using minimum spanning tree to reduce the computational overhead. In the first step, the algorithm partitions the dataset into large number of sub-clusters based on dispersion of data points to capture the geometry of clusters. After partitioning the dataset, a minimum spanning tree based on the centroids of each of the sub-clusters is constructed to identify the adjacent pairs. A novel merge method is proposed to find the genuine clusters by repeatedly merging the adjacent sub-clusters. The cohesion and intra-similarity are introduced to compute the level of dispersion of data points with respect to the centers of an adjacent pair and average edge weight of a sub-clusters respectively. The algorithm takes O(N3/2) time which is a N factor improvement over the popular hybrid clustering algorithms. Experimental analyses on both synthetic as well as gene expression datasets demonstrate that the proposed technique shows significant improvement over competing clustering algorithms in terms of execution time and improved cluster quality. Moreover, the proposed algorithm does not require any user defined parameters and it can estimate the number of clusters more accurately.

A General Hybrid Clustering Technique

Here, we propose a clustering technique for general clustering problems including those that have nonconvex clusters. For a given desired number of clusters K, we use three stages to find clusters. The first stage uses a hybrid clustering technique to produce a series of clusterings of various sizes (randomly selected). The key step in this stage is to find a K-means clustering using clusters where and then join these small clusters by using single linkage clustering. The second stage stabilizes the result of stage one by reclustering via the “membership matrix” under Hamming distance to generate a dendrogram. The third stage is to cut the dendrogram to get clusters where and then prune back to K to give a final clustering. A variant on our technique also gives a reasonable estimate for KT, the true number of clusters. We provide arguments to justify the steps in the stages of our methods and we provide examples involving simulated and published data to compare our technique with other techniques. An R library, GHC, implementing our method is available from Github.

Reducing Handoff Delay in SDN-based 5G Networks Using AP Clustering

Densification is one of the 5G networks solutions to increase data rate and network capacity. This technology aims for constructing a large number of small cells in a specific area which may include different wireless technologies (LTE, IEEE 802.11, WiMAX, etc.). Small cells provide shorter distance between the cell access point and the mobile node which will reduce the signal attenuation and noise. This could generate a better connection, which goes along with the expectations of high data rate requests for the year 2020. The downside of the densification is that frequent handoffs will be a challenge for the network nodes. A software-defined network SDN, on the other hand, is another promising solution for 5G networks which controls the network through centralization. Using these two solutions, the handoffs in the network are expected to be frequent and hence a more delay will be added. To reduce such delay, a distributed-mobility-management technique is used in SDN-based networks, where the mobile node takes the handoff decision. This paper has focused on the issue of the distributed-handoff delay and handover failure of mobile nodes in SDN-based IEEE 802.11 network. It proposes a hybrid clustering technique using K-mean and genetic algorithm to cluster the network. The clustering aims to reduce the scanning phase of the mobile node by minimizing the number of access points that need to be scanned for the handoff. The results show lower delays and a reduced number of handoff failure compared to the un-clustered network.

A Two-Stage Stochastic Framework for an Electricity Retailer Considering Demand Response and Uncertainties Using a Hybrid Clustering Technique

Due to the highly volatile prices of pool market, a main source of an electricity retailer to meet its clients’ demand, retailers generally sign forward contracts in order to protect themselves from being exposed to the risk imposed by the uncertain pool prices. These contracts, however, decrease the retailer’s expected profit owing to their higher average prices compared with the pool market. In this paper, focusing on price-based demand response programs, a two-stage scenario-based stochastic framework is presented for the medium-term decision-making problem of an electricity retailer. This study would demonstrate that demand response programs can be an effective tool to hedge against the risk and an appropriate alternative yielding less involvement in costly forward agreements. The proposed model decides the optimal level of participation in the pool as well as forward market and determines the electricity rates offered to the clients. The objective is maximizing the expected value of the retailer’s profit, whereas the exposure risk is confined to a pre-specified level. Moreover, the scenarios required for the stochastic programming problem are generated using a hybrid clustering technique based on K-means and particle swarm optimization algorithms. The proposed model is mathematically described as a mixed-integer linear problem which is solvable through commercial software packages. The efficiency of the provided approach is evaluated via a realistic case study according to the available data from Spain electricity market.