Efficient Clustering Method Research Articles

A multi-view clustering framework via integrating K-means and graph-cut

Graph-cut and K-means are two classical clustering methods, which are used by most of existing clustering methods. First, a multi-view clustering framework via K-means and graph is proposed and a efficient clustering method is developed based on this framework. Our method calculates the discrete cluster assignment matrix directly and includes the auto-weighted strategy to consider the different contributions of views. Additionally, by generating the distance matrices for views with the corresponding graphs, our method fits both Gaussian and non-Gaussian distributed datasets. Moreover, the proposed algorithm is able to obtain robust results because it does not need initializing and computing cluster centroids. Extensive experiments on a synthetic dataset and four real-world datasets indicate the effectiveness of our method.

Spectral clustering based on the local similarity measure of shared neighbors

Spectral clustering has become a typical and efficient clustering method used in a variety of applications. The critical step of spectral clustering is the similarity measurement, which largely determines the performance of the spectral clustering method. In this paper, we propose a novel spectral clustering algorithm based on the local similarity measure of shared neighbors. This similarity measurement exploits the local density information between data points based on the weight of the shared neighbors in a directed k $$ k $$ -nearest neighbor graph with only one parameter k $$ k $$ , that is, the number of nearest neighbors. Numerical experiments on synthetic and real-world datasets demonstrate that our proposed algorithm outperforms other existing spectral clustering algorithms in terms of the clustering performance measured via the normalized mutual information, clustering accuracy, and F $$ F $$ -measure. As an example, the proposed method can provide an improvement of 15.82% in the clustering performance for the Soybean dataset.

A Study on the Impact of Multiview Distributed Feature Coding on a Multicamera Vehicle Tracking System at Roundabouts

Visual sensor networks are one potential enabler for the evolution of the Internet of things. Due to their limited resources in terms of energy and bandwidth, it is crucial to identify appropriate approaches that take into considerations such constraints and reduce the amount of data transmitted to the gathering point (sink). In this context, this paper describes the impact of a distributed smart-camera system that exploits an analyze-then-compress strategy, on a multi-view vehicle tracking at roundabouts application. In the tested system, part of the processing is shifted to the smart cameras, i.e., the object detection/classification and feature extraction, so that only the extracted features describing moving vehicles are transmitted instead of the whole image/video. Features are further compacted by using a state-of-the-art distributed coding technique, based upon an efficient clustering method that exploits the temporal and spatial (multiple views) correlations between features. The system is tested on a real-data scenario, by evaluating the bit-rate reduction capabilities in dependence of the channel conditions, as well as the matching accuracy of the reconstructed descriptors in the specific tracking application. Both feature-wise and object-wise matching are investigated. For the chosen application scenario, a bit-rate reduction of 30 – 35% is proved to be achievable in non-ideal channel conditions. Even more interestingly, such reduction is proved not to harm the matching accuracy (i.e., it is coherent with the target application), for which an F-score up to 0.923 is guaranteed.

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

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

MRI Brain Tumor Segmentation with Intuitionist Possibilistic Fuzzy Clustering and Morphological Operations

Digital Image Processing (DIP) is a well-developed field in the biological sciences which involves classification and detection of tumour. In medical science, automatic brain tumor diagnosis is an important phase. Brain tumor detection is performed by Computer-Aided Diagnosis (CAD) systems. The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes. Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research. Brain tumor diagnosis mainly performed for obtaining exact location, orientation and area of abnormal tissues. Cancer and edema regions inference from brain magnetic resonance imaging (MRI) information is considered to be great challenge due to brain tumors complex structure, blurred borders, besides exterior features like noise. The noise compassion is mainly reduced along with segmentation stability by suggesting efficient hybrid clustering method merged with morphological process for brain cancer segmentation. Combined form of Median Modified Wiener filter (CMMWF) is chiefly deployed for denoising, and morphological operations which in turn eliminate nonbrain tissue, efficiently dropping technique’s sensitivity to noise. The proposed system contains the main phases such as preprocessing, brain tumor extraction and post processing. Image segmentation is greatly achieved by presenting Intuitionist Possibilistic Fuzzy Clustering (IPFC) algorithm. The algorithm’s stability is greatly enhanced by this clustering along with clustering parameters sensitivity reduction. Then, the post processing of images are done through morphological operations along with Hybrid Median filtering (HMF) for attaining exact tumors representations. Additionally, suggested algorithm is substantiated by comparing with other existing segmentation algorithms. The outcomes reveal that suggested algorithm achieves improved outcomes pertaining to accuracy, sensitivity, specificity, and recall.

A generative node-attribute network model for detecting generalized structure and semantics

A challenge of community detection in attributed networks is how we can design an effective and efficient clustering method that can not only discover a wide of structure types but also have good community semantic annotations. To this end, by sharing the latent position of nodes, a mathematically principled model (named GNAN) that fuses topological information and node-attribute information is developed. Using the expectation–maximization algorithm, the latent position of each node and the model parameters are learned. The new model detects communities more accurately than can be done with topology information alone. And a case study is provided to show the ability of our model in the semantic interpretability of communities. In detail, firstly, inspired by the idea of NMM (Newman’s Mixture Models), a group of parameters that characterize the link behaviors of nodes is introduced into the topological model. In the probabilistic sense, nodes with the same link pattern form a community. Therefore, the combined model can generate not only traditional communities, i.e., groupings of nodes with dense internal connections and sparse external ones, but also a range of other types of structure in networks, such as bipartite structure, core–periphery structure, and their mixture structure, which are collectively referred to as generalized structure. Secondly, based on the homogeneity assumption, another group of parameters describing the distribution of attributes in a community is introduced into the attributed model. Under the control of these parameters, the united model can generate different attributes according to the probability, and automatically discover the critical attributes of the community. Finally, experiments on both synthetic and real-world networks with various network structures show that the new model can detect communities more accurately than the related state-of-the-art models.

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity.

Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process because of the existence of countless properties inherent in color images that can hamper performance. Due to diversified color image properties, a method that is appropriate for one category of images may not necessarily be suitable for others. The selection of image abstraction is a decisive preprocessing step in saliency computation and region-based image abstraction has become popular because of its computational efficiency and robustness. However, the performances of the existing region-based salient object detection methods are extremely hooked on the selection of an optimal region granularity. The incorrect selection of region granularity is potentially prone to under- or over-segmentation of color images, which can lead to a non-uniform highlighting of salient objects. In this study, the method of color histogram clustering was utilized to automatically determine suitable homogenous regions in an image. Region saliency score was computed as a function of color contrast, contrast ratio, spatial feature, and center prior. Morphological operations were ultimately performed to eliminate the undesirable artifacts that may be present at the saliency detection stage. Thus, we have introduced a novel, simple, robust, and computationally efficient color histogram clustering method that agglutinates color contrast, contrast ratio, spatial feature, and center prior for detecting salient objects in color images. Experimental validation with different categories of images selected from eight benchmarked corpora has indicated that the proposed method outperforms 30 bottom-up non-deep learning and seven top-down deep learning salient object detection methods based on the standard performance metrics.

Incremental multi-view spectral clustering with sparse and connected graph learning

In recent years, a lot of excellent multi-view clustering methods have been proposed. Because most of them need to fuse all views at one time, they are infeasible as the number of views increases over time. If the present multi-view clustering methods are employed directly to re-fuse all views at each time, it is too expensive to store all historical views. In this paper, we proposed an efficient incremental multi-view spectral clustering method with sparse and connected graph learning (SCGL). In our method, only one consensus similarity matrix is stored to represent the structural information of all historical views. Once the newly collected view is available, the consensus similarity matrix is reconstructed by learning from its previous version and the current new view. To further improve the incremental multi-view clustering performance, the sparse graph learning and the connected graph learning are integrated into our model, which can not only reduce the noises, but also preserve the correct connections within clusters. Experiments on several multi-view datasets demonstrate that our method is superior to traditional methods in clustering accuracy, and is more suitable to deal with the multi-view clustering with the number of views increasing over time.

Real time flood disaster monitoring based on energy efficient ensemble clustering mechanism in wireless sensor network

AbstractIn this article, energy efficient ensemble clustering method (EECM) with black widow optimization (EECM‐BWO) algorithm is proposed for effective data transmission with the help of real time flood disaster monitoring wireless sensor network (WSN). Initially, unified scalable ensemble clustering algorithm based on ensemble generation and consensus function is proposed for selecting the optimal routing path among the node using BWO algorithm. Then, biologically inspired routing black widow spiders optimization algorithm is proposed to trade off the nodes energy level, self‐organization, and self‐configuration in the WSN. The simulation is performed using NS2 simulator for validating the performance of the proposed EECM‐BWO method. Here, in node, low delay achieves 24.07%, 72.58%, 51.36%, 81.75%, 77.74%, high packet delivery ratio achieves 70.83%, 53.93%, 90.23%, 43.58%, 24.58%, low packet drop attains 77.93%, 72.76%, 61.56%, 51.87%, 34.35%, low energy consumption attains 75.9%, 52.94%, 65.81%, 58%, 41.2% compared with existing energy‐efficient clustering approach consolidated game theory as well as dual‐cluster‐head mode for WSNs energy‐aware clustering by cuckoo optimization approach (EECM‐COA), energy‐aware clustering‐based routing using multi‐path reliable transmission with routing and control board (EECM‐RCB‐MRT), adaptive repair algorithm with temporally ordered routing algorithms for flood control strategy (EECM‐AR‐TORA‐FCS), passive multi‐hop clustering algorithm (EECM‐PMC), dynamic source routing protocol based on genetic algorithm‐bacterial foraging optimization (DSR‐GA‐BFO).

A New Initialization Method for K-means Algorithm Based on Clustering Coefficient

The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm uses a random initialization method that does not guarantee unique clustering results. In this paper, a novel method for selecting initial cluster centers on the basis of a complex network is proposed. The data set to be clustered is represented as a complex network. The clustering coefficients of a vertex and those of its adjacent vertices are used to introduce the novel concepts of “valley point” and “peak point,” which can be used to construct a set of candidates of the initial cluster centers. Finally, an algorithm for selecting the initial cluster centers from the constructed set of candidates is proposed. The time complexity of the proposed algorithm is O(n 2), where n is the number of data points. The proposed algorithm is applied to four data sets with different dimensions to compute the initial cluster centers for the k-means algorithm. Compared with the random initialization and maximin methods, the proposed algorithm demonstrates superior clustering performance in obtaining the initial cluster centers for the k-means algorithm.

An Intelligent Clustering Technique for Analysing the Performance of Students during Lockdown Period of Covid-19

Corona virus or simply Corona is the current leading pandemic of the world. It has affected students and their in education in higher numbers than any other sector putting them into a depression. Hence this research attempts to suggest solutions for reducing depression amongst students amidst the pandemic. This work proposes ESVMs (Enhanced Support Vector Machines) model for its predictions. Identifying student performances is complex issue as the numbers are voluminous and hence the objective of this research is to assess student performance prediction model by using an efficient clustering method. Missing values and irrelevant data are resolved in this work using SCCs (Statistical correlation Coefficients) which work on subject wise manner or student wise data. This work also provides a novel solution for data pre-processing. IFCM (Improved Fuzzy C-means clustering) proposed in this work identifies high quality clusters with robustness. Further, the use of PSO (Particle Swarm Optimization) in feature selections improves its efficiency of the given data. Classifications are executed by the proposed ESVMs which predicts student's grade with accuracy. The evaluation results of this study improve classification accuracy significantly when compared to existing prediction models. © 2021 Karadeniz Technical University. All rights reserved.

An Interband Registration Method for Hyperspectral Images Based on Adaptive Iterative Clustering

In the context of the problem of image blur and nonlinear reflectance difference between bands in the registration of hyperspectral images, the conventional method has a large registration error and is even unable to complete the registration. This paper proposes a robust and efficient registration algorithm based on iterative clustering for interband registration of hyperspectral images. The algorithm starts by extracting feature points using the scale-invariant feature transform (SIFT) to achieve initial putative matching. Subsequently, feature matching is performed using four-dimensional descriptors based on the geometric, radiometric, and feature properties of the data. An efficient iterative clustering method is proposed to perform cluster analysis on the proposed descriptors and extract the correct matching points. In addition, we use an adaptive strategy to analyze the key parameters and extract values automatically during the iterative process. We designed four experiments to prove that our method solves the problem of blurred image registration and multi-modal registration of hyperspectral images. It has high robustness to multiple scenes, multiple satellites, and multiple transformations, and it is better than other similar feature matching algorithms.

Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model.

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter . Therefore, we can simply set or for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

Towards an efficient real-time kernel function stream clustering method via shared nearest-neighbor density for the IIoT

The rapid development of 5G communication technology will considerably help the expansion and gth Industrial Internet of things (IIoT). Indeed, both the data scale and dimension will significantly increase, leading to a challenging problem of the effective real-time stream clustering in the field of IIoT streaming mining. This paper proposes an efficient and novel real-time kernel function stream clustering method based on shared nearest-neighbor density for IIoT. In the proposed method, the projection technology is used to select the dimensions of high-dimensional data, while the Euler kernel function is used as the similarity measure. Furthermore, the micro-clusters are divided by the shared nearest-neighbor density, and the outliers are relearned. The main innovation lies in using the Euler kernel function to measure the similarity, reduce the sensitivity of outliers, and use the relearning strategy to improve the clustering quality of the data stream. The theoretical analysis and experimental comparisons on the simulated data sets show that the proposed method is very effective and represents a good solution for clustering real-time data streams of IIoT.

Data clustering based probabilistic optimal scheduling of an energy hub considering risk-averse

An energy hub (EH) is a multi-carrier energy system supplying various types of energy demands. Optimal management of these systems is a non-linear, non-convex, and complicated problem. This complexity is increased because of the unpredictable renewable generation and consumption patterns. Inattention to the probabilistic nature of the uncertain variables may increase the risk of encountering undesired conditions. The use of accurate and low computational probabilistic assessment methods is very important in this problem.This paper presents risk-constrained stochastic scheduling for an EH considering the uncertainties of renewable generations and load demands. The risk is assessed by the conditional value at risk (CVaR) method. A trade-off between decrement of the operation and emissions cost and increment of the risk aversion is offered. The proposed method is applied on an energy hub consisting of a wind turbine (WT), photovoltaic (PV) cells, a fuel cell power plant (FCPP), a combined heat and power generation unit (CHP) and plug-in electric vehicles (PEVs). The wind speed, solar irradiation, all types of demands as well as the market prices are considered as uncertain variables. In order to get maximum profit and enhance the consumption curve, electrical, thermal and cooling demand response programs (DRPs) are applied. Uncertainties in input random variables are managed by the efficient k-means data clustering method. The results, which show considerable flexibility in the energy management of the energy hub, are comprehensively discussed. Simulation results indicate that 1.97%, 6.25%, and 10.17% reduction in the operation cost of the proposed EH can be achieved with the integration of the PEVs, FCPP, and DRPs, respectively. Additionally, the risk cost of the EH is improved by 1.95%, 6.2%, and 9, 68% with consideration of the PEVs, FCPP, and DRPs, respectively.

Multi-Material Interface Extraction and its Topological Relationship Expression for 3-D Image Data

The interface extraction and its topological relationship is a critical step toward the visualization, recognition, and feature retrieval of multi-material objects. The existing methods pay attention only to the interface visualization and extraction, while ignoring the representation of the interface topological relationship. In this article, we proposed a multi-material interface extraction and its topological relationship expression method for 3-D image data. First, we extend the 2-D Canny detector to 3-D for extracting the interface points of 3-D image data. Second, we introduce an efficient clustering method to cluster interface points for obtaining the sub-interfaces. Third, the neighboring relationship of each sub-interface is calculated and the directed skeleton tree with global topological feature is constructed. Finally, we propose a matrix representation to explicitly express the topological relationship of interfaces. Experimental results demonstrate that our method can efficiently extract interfaces for a variety of synthetic and raw scanned 3-D image data, even in the presence of noisy.

Multi-Hop Dynamic Map Data Propagation Algorithm for Clustered Vehicular Networks

To ensure the driving safety in vehicular network, it is necessary to construct a local dynamic map (LDM) for an extended range. Using the standard vehicular communication protocols, however, vehicles can construct the LDM for only one-hop range. Constructing large-scale LDM is highly challenging because vehicles randomly change their position. This paper proposes a dynamic map propagation (DMP) method, which builds a large aggregated LDM data using a multi-hop communication. To reduce the data overhead, we introduce an efficient clustering method based on a half-circle of the forwarder’s wireless range. The DMP elects one forwarder per cluster, which constructs LDM and forwards it to a neighbor cluster. The inter-cluster interference is minimized by allocating a different transmit window to each cluster. DMP copes with a dynamic environment by frequently re-electing the forwarders and their associated transmission windows. Simulation results reveal that DMP enhances the forwarders’ reception ratio by 20%, while extending LDM dissemination range by 29% over a previous work.

A three-way density peak clustering method based on evidence theory

Density peaks clustering (DPC) algorithm is an efficient and simple clustering method attracting the attention of many researchers. However, its strategy of assigning each non-grouped object to the same cluster depends on its nearest neighbors having a higher local density. This may lead to the cluster label error propagation problem, i.e. if an object is wrong-labeled during the clustering process, its label will be propagated in the subsequent assignment. To overcome this defect, in this paper we propose a three-way density peak clustering method based on evidence theory, referred to as 3W-DPET. 3W-DPET forms clusters as interval sets using three-way clustering representation including three disjoint regions called positive region (POS), boundary region (BND) and negative region (NEG). 3W-DPET mainly consists of three steps: (1) finding out cluster centers and noises before clustering; (2) using a midrange distance comparison method to detect positive regions of clusters; and (3) allocating the remaining non-grouped objects, including noises, to the boundary region or the negative region of clusters. The distinguishing feature of 3W-DPET is that evidence theory is used to construct and collect the information of K-nearest neighbors in order to assign non-grouped objects to the most suitable cluster, which can effectively solve the problem of cluster label error propagation. In order to validate 3W-DPET, we test it on 18 datasets using three benchmarks (ACC, ARI and NMI), and compare it to K-means, FCM, DPC, KNN-DPC, DPCSA, SNN-DPC and CE3-kmeans methods. Experimental results suggest that 3W-DPET can effectively find clusters and its results conform with human cognition.

A Unified NMPC Scheme for MAVs Navigation With 3D Collision Avoidance Under Position Uncertainty

This article proposes a novel Nonlinear Model Predictive Control (NMPC) framework for Micro Aerial Vehicle (MAV) autonomous navigation in constrained environments. The introduced framework allows us to consider the nonlinear dynamics of MAVs and guarantees real-time performance. Our first contribution is to design a computationally efficient subspace clustering method to reveal from geometrical constraints to underlying constraint planes within a 3D point cloud, obtained from a 3D lidar scanner. The second contribution of our work is to incorporate the extracted information into the nonlinear constraints of NMPC for avoiding collisions. Our third contribution focuses on making the controller robust by considering the uncertainty of localization and NMPC using the Shannon entropy. This step enables us to track either the position or velocity references, or none of them if necessary. As a result, the collision avoidance constraints are defined in the local coordinates of MAVs and it remains active and guarantees collision avoidance, despite localization uncertainties, e.g., position estimation drifts. Additionally, as the platform continues the mission, this will result in less uncertain position estimations, due to the feature extraction and loop closure. The efficacy of the suggested framework has been evaluated using various simulations in the Gazebo environment.

Interacting T-S fuzzy semantic model estimation for maneuvering target tracking

This paper proposes an interacting Takagi–Sugeno(T-S) fuzzy semantic model estimator (ITS-FSM) for maneuvering target tracking, and constructs a framework for a generic interacting T-S fuzzy semantic model to incorporate semantic information concerning the target. To adaptively calculate the transition probability matrix of the models, a probabilistic switching model based on semantic fuzzy sets is derived by using the degree of intersection between fuzzy sets, which enables switching from one semantic fuzzy set to another. We also propose an efficient kernel maximum entropy fuzzy clustering method to identify the premise parameters of the model. This enables the proposed algorithm to recursively estimate the premise parameters in case of a limited number of samples. Moreover, a modified extended forgetting factor recursive least squares (MEFRLS) estimator is used to identify the parameters of the T-S fuzzy semantic model. The results of experiments on three simulation datasets show that the proposed ITS-FSM algorithm is efficient, and is excellent at handling non-Gaussian noise.