Clustering By Fast Search And Find Of Density Peaks Research Articles

Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks

Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this article, we propose an improved soft- $k$ -means (IS- $k$ -means) clustering algorithm to balance the energy consumption of nodes in WSNs. First, we use the idea of clustering by fast search and find of density peaks (CFSFDPs) and kernel density estimation (KDE) to improve the selection of the initial cluster centers of the soft $k$ -means clustering algorithm. Then, we utilize the flexibility of the soft- $k$ -means and reassign member nodes considering their membership probabilities at the boundary of clusters to balance the number of nodes per cluster. Furthermore, the concept of multicluster heads is employed to balance the energy consumption within clusters. Extensive simulation results under different network scenarios demonstrate that for small-scale WSNs with single-hop transmission, the proposed algorithm can postpone the first node death, the half of nodes death, and the last node death on average when compared to various clustering algorithms from the literature.

Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

Research on the optimal layout of pressure monitoring points in water-cooling pipelines of nuclear fusion device based on fusion distance

In view of the safety and stability requirements of the cooling water system (CWS) of the nuclear fusion device, it is necessary to optimize the pressure monitoring points of the water-cooling pipelines. To optimize the layout of pipeline pressure monitoring points, a method based on clustering by fast search and find of density peaks (CFSFDP) algorithm is proposed. By combining the fusion distance of the pipeline spatial attributes and the efficient CFSFDP clustering algorithm, the layout of the pressure monitoring points in the pipeline network can be effectively optimized. Further simulations and analysis show that this method can quickly realize the optimal layout of pressure monitoring points, and the rationality and effectiveness of the fusion distance is verified. Compared with other typical clustering algorithms, the CFSFDP algorithm for cluster analysis reflects the high efficiency advantages.

An Adaptive Pulse Separation Strategy for PD Detection in Frequency-Tuned Resonant Tests

Frequency-tuned resonant (ACRF) test is one of the most widely used technologies for power equipment diagnosis. However, the intense interference generated by the variable-frequency power source hinders its further application in partial discharge (PD) detection. For this reason, an adaptive PD separation method based on improved clustering by fast search and find of density peaks (CFSFDP) is proposed in this article to separate PD signals and disturbances under the ACRF test. First, the characteristics of the PD signals and the interference signals generated from the ACRF test system are analyzed and compared. Then, an improved CFSFDP algorithm is proposed to realize the automatic cluster center selection and signal separation, and the working principle of the PD separation strategy based on the improved CFSFDP algorithm is introduced. Finally, the effectiveness of the proposed method is validated by performing the ACRF test on a defective cable. The experimental results show that this method can realize the separation and detection of PD signals in ACRF tests. The proposed method provides a feasible solution for performing PD detection using the ACRF test system without the restriction of the test system structure.

A GGIW-PHD Filter for Multiple Non-Ellipsoidal Extended Targets Tracking With Varying Number of Sub-Objects

When the extension state of the non-ellipsoidal extended target (NET) changes, the performance of traditional multiple target tracking algorithms based on the constant number of sub-objects will decrease. To solve this problem, this paper proposes a gamma Gaussian inverse Wishart probability hypothesis density filter for non-ellipsoidal extended targets with varying number of sub-objects, called VN-NET-GGIW-PHD filter. In the proposed filter, each NET is considered as a combination of multiple spatially close sub-objects, and the label management is introduced to realize the association between the NET and corresponding sub-objects. Then, by target spawning and combination, the number of sub-objects for approximating the extension state of each NET can be adjusted automatically. Furthermore, to obtain the partition of the measurement set, an approach based on the clustering by fast search and find of density peaks (CFSFDP) algorithm and expectation maximization (EM) algorithm is proposed. Simulation results show that the proposed filter can adaptively adjust the number of sub-objects and has better performance when the extension state of the NET changes.

Underdetermined mixing matrix estimation based on joint density-based clustering algorithms

In underdetermined blind source separation (UBSS), the estimation of the mixing matrix is crucial because it directly affects the performance of UBSS. To improve the estimation accuracy, this paper proposes a joint clustering analysis method based on density based spatial clustering of applications with noise (DBSCAN) and clustering by fast search and find of density peaks (CFSFDP). In the reprocessing, the observed signals in the time domain are transformed into sparse signals in the frequency domain through a short time Fourier transform (STFT), and single-source-point (SSP) detection is used to enhance the linear clustering characteristic of signals. In addition, to facilitate the use of density-based clustering analysis, mirroring mapping is used to transform the linear clustering into compact clustering on the positive half unit circle (or sphere). For the estimation of the underdetermined mixing matrix (UMM), the DBSCAN algorithm is first used to search for high-density data points, and automatically find the number of clusters and the cluster centers; then, the CFSFDP algorithm is used to search the density peaks of the data clusters, so as to further modify the cluster centers. Because each cluster center corresponds to a column vector of the mixing matrix, the proposed algorithm can estimate the UMM through cluster analysis. The simulation results show that the proposed algorithm can not only improve the estimation accuracy of the UMM, but also provide a more robust estimator. In addition, the joint clustering method also makes up for the shortcomings of the CFSFDP algorithm that requires human intervention.

Is haze control effective? A research based on regional public perception differences

Abstract Background Beijing-Tianjin-Hebei region (BTHR) is a world-class city group in China. However, haze severity of BTHR ranked in the top in China, 2014. As haze has certain impact on people's physical and psychological health, government has taken various measures to actively manage it in recent years. We try to answer the question: From public perception, whether haze control in BTHR has achieved a little success? Methods According to region and gender, we select four groups as respondents: BTHR and non-BTHR men, BTHR and non-BTHR women. We investigate their perception towards haze related issues. After collecting 400 anonymous questionnaires, we use clustering by fast search and find of density peaks (CFSFDP) algorithm to divide the investigated population into different clusters that represent different perception situations. Then, we test whether the frequency of each group divided into four clusters was consistent with the overall distribution frequency by fitting test. Results Questionnaires are automatically divided into four clusters by CFSFDP. The results of test (p > 0.1) show that the four groups are consistent (no significant difference) with overall clustering. Overall average score for “public perception to haze control” is 2.974 (full-score is 5.000), belonging to middle and upper level. Conclusions The cluster results show that there is no significant difference in the perception of haze control effect among four groups. Therefore, for the public in BTHR, there is no significant difference in their recognition of haze control compared with that in other regions, and the overall scores are all at the middle and upper levels. From public perception, effect of haze control in BTHR is in line with that in other regions. Key messages Using clustering algorithm and statistical test to identify the difference of public perception of haze control. From the perspective of public perception, haze control in BTHR has achieved initial results.

Density Peak Clustering Algorithm Considering Topological Features

The clustering algorithm plays an important role in data mining and image processing. The breakthrough of algorithm precision and method directly affects the direction and progress of the following research. At present, types of clustering algorithms are mainly divided into hierarchical, density-based, grid-based and model-based ones. This paper mainly studies the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm, which is a new clustering method based on density. The algorithm has the characteristics of no iterative process, few parameters and high precision. However, we found that the clustering algorithm did not consider the original topological characteristics of the data. We also found that the clustering data is similar to the social network nodes mentioned in DeepWalk, which satisfied power-law distribution. In this study, we tried to consider the topological characteristics of the graph in the clustering algorithm. Based on previous studies, we propose a clustering algorithm that adds the topological characteristics of original data on the basis of the CFSFDP algorithm. Our experimental results show that the clustering algorithm with topological features significantly improves the clustering effect and proves that the addition of topological features is effective and feasible.

Research on the multiple fuzzy parametric fuzzy sets and its framework of clustering algorithm

This article first introduces the current research situation of the theory of fuzzy sets, and then, on the basis of type-1 fuzzy sets, according to the different forms and numbers of the parameters of membership functions, put forward the definitions of classical parametric fuzzy sets, multiple classical parametric fuzzy sets, semi fuzzy parametric fuzzy sets, fuzzy parametric fuzzy sets, multiple fuzzy parametric fuzzy sets, etc. Their mathematical expressions are given. On this basis, the multiple parameter fuzzy sets are compared with type-2 fuzzy sets, interval-valued type-2 fuzzy sets, intuitionistic fuzzy sets and interval-valued intuitionistic fuzzy sets. Subsequently, based on clustering by fast search and find of density peaks (CFSFDP), the method and steps of multiple fuzzy parametric fuzzy sets clustering, called “clustering for multiple fuzzy parametric fuzzy sets by fast search and find of density peaks” (MFPFS-CFSFDP), which is further discussed and proposed. In order to reduce the computational complexity of MFPFS-CFSFDP algorithm, referring to the concept of granularity and combining the characteristics of multiple fuzzy parametric fuzzy sets, we propose a “minimum granularity-based key parametric method” to improve the MFPFS-CFSFDP algorithm. Furthermore, two clustering algorithms, which called “clustering plus for multiple fuzzy parametric fuzzy sets by fast search and find of density peaks” (MFPFS-CFSFDP+) and “clustering plus plus for multiple fuzzy parametric fuzzy sets by fast search and find of density peaks” (MFPFS-CFSFDP++) are proposed. Finally, taking the multiple normal fuzzy parametric normal fuzzy sets as an example, the algorithm framework tables of these three clustering algorithms are given, and their advantages and disadvantages are summarized, they provide a theoretical basis for further research on clustering for multiple fuzzy parametric fuzzy sets.

Security Constrained Unit Commitment with Extreme Wind Scenarios

The rapid development of economy and society stimulates the increase of power demand. Wind power has received great attention as a typical renewable energy, and the share of wind power is continually increasing in recent years. However, the high integration of wind power brings challenges to the secure and reliable operation of power grid due to the intermittent characteristic of wind power. In order to solve the operation risk caused by wind power uncertainty, this paper proposes to solve the problem of stochastic security-constrained unit commitment (SCUC) by considering the extreme scenarios of wind power output. Firstly, assuming that the probability density distribution of wind power approximately follows a normal distribution, a great number of scenarios are generated by Monte Carlo (MC) simulation method to capture the stochastic nature of wind power output. Then, the clustering by fast search and find of density peaks (CSFDP) is utilized to separate the generated scenarios into three types: extreme, normal and typical scenarios. The extreme scenarios are identified to determine the on/off statuses of generators, while the typical scenarios are used to solve the day-ahead security-constrained economic dispatch (SCED) problem. The advantage of the proposed method is to ensure the robustness of SCUC solution while reducing the conservativeness of the solution as much as possible. The effectiveness of the proposed method is verified by IEEE test systems.

Automatic identification and characterization of discontinuities in rock masses from 3D point clouds

The routine application of remote surveying techniques which can quickly acquire 3D digital data with high resolution, in particular digital photogrammetry, light detection and ranging (LiDAR) and unmanned aerial vehicle (UAV) for rock mass characterization has rapidly grown over the past decade. In this paper, a new method for automatic identification and interpretation of rock mass discontinuities, clustering of discontinuity sets and characterization of discontinuity orientation, persistence and spacing using 3D point clouds, is presented. The proposed method is based on a four-stage procedure consisting of: (1) normal vector calculation using the iterative reweighted plane fitting (IRPF) method, (2) discontinuity sets clustering by fast search and find of density peaks (CFSFDP) algorithm, and Fisher’s K value iterative calculation to eliminate noise points, (3) discontinuity segmentation using density-ratio based method, and discontinuity plane fitting using the random sample consensus (RANSAC) algorithm, (4) persistence and spacing calculation using the theory of analytic geometry. The method is applied to two case studies (i.e. rock slopes) and compared with the results from previous studies and from manual survey. It is concluded that the proposed method is reliable and yields a great accuracy for automatic identification of discontinuities in rock masses.

An Incremental Self-Adaptive Wood Species Classification Prototype System

The present wood species classification systems can usually process the limited wood species quantity. We propose a novel incremental self-adaptive wood species classification system to solve the above-mentioned issue. The visible/near-infrared (VIS/NIR) spectrometer is used to pick up the spectral curves of wood samples for the subsequent wood species classification. First, when new wood samples of unknown wood species are added, they are classified as an unknown category by our one-class classifier, Support Vector Data Description (SVDD), while the existent wood species are classified as a known category by the SVDD. Second, the wood samples of known species are sent into the BP neural network for subsequent wood species classification. Third, the new wood samples of unknown species are sent into the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm for the unsupervised clustering, and the clustering result is evaluated by the internal and external norms. Last, if one cluster of one unknown species has an adequate amount of wood samples, these wood samples are removed and identified by human experts or other schemes to ensure to get the correct wood species name. Then, these wood samples are considered as a new known species and are sent into the classifiers, SVDD and BP neural network, to train them again. Experiments on 13 wood species prove the effectiveness of our prototype system with an overall classification accuracy of above 95%.

Grouping Method of Semiconductor Bonding Equipment Based on Clustering by Fast Search and Find of Density Peaks for Dynamic Matching According to Processing Tasks

Given that the equipment for the semiconductor packaging line adopts the fixed grouping production method, thus failing to dynamically match the processing task demand capacity, in the present study, we proposed a semiconductor bonding equipment-grouping method based on processing task matching. This method sets the device group closed position constraint and the matching constraint between the device type and the processing type and uses the graph theory method to establish the device grouping model. The dynamic grouping of equipment under the capacity demand of different processing tasks was achieved by changing the relationship matrix between devices. The drawback of this grouping method is rather large grouping deviation, which we tried to solve with the clustering by fast search and find of density peaks (CFSFDP) that was added to cluster the sets of attribute information of the devices so as to obtain the maximum number of grouping groups obtained to reduce the grouping deviation. Simulation comparison experiments were carried out under different circumstances considering the size of the formation, the distribution of demand capacity, and the coefficient of difference in demand capacity. Compared with the standard device grouping method, the grouping method based on semiconductor bonding equipment and CFSFDP algorithm for dynamic matching according to processing tasks had better performance in solving the dynamic grouping problem.

Sparse learning based on clustering by fast search and find of density peaks

Clustering by fast search and find of density peaks (CFSFDP) is a novel clustering algorithm proposed in recent years. The algorithm has the advantages of low computational complexity and high accuracy. However, the truncation distance dc needs to be determined according to user experience. Aiming to overcome these drawbacks, this paper proposes a new algorithm named Sparse learning based on clustering by fast search and find of density peaks (SL-CFSFDP). Compared to CFSFDP, the proposed algorithm can obtain dc automatically, and it uses sparse learning to determine the neighbors of each data point, removing irrelevant data points at the same time. SL-CFSFDP combines the local density and the distance δi to automatically determine cluster centers, after which the remaining data points are assigned to clusters according to the local density and distance δi. Extensive experimental results on both synthetic and benchmark datasets show that SL-CFSFDP is superior to DBSCAN and CFSFDP.

A new local density and relative distance based spectrum clustering

A novel local density and relative distance-based spectrum clustering (LDRDSC) algorithm is proposed for multidimensional data clustering. The density spectra consider both redefined local densities and relative distances. The spectral peaks are defined as cluster centers since these peaks correspond to the local density maximums. Different clusters correspond to different spectra. The clustering by fast search and find of density peaks (CFSFDP) algorithm and several benchmark data sets are employed to validate our proposed LDRDSC algorithm. Once the density spectrum is generated, the rest points can be automatically clustered by our LDRDSC algorithm, which is different from CFSFDP. CFSFDP needs to categorize data points according to the cluster centers. Furthermore, our LDRDSC algorithm is compared with other five typical clustering algorithms (DBSCAN, FCM, AP, Mean Shift and k-means) in order to validate the effectiveness of the proposed algorithm. Computational results demonstrate that our algorithm can obtain a better clustering result than the above mentioned algorithms, especially in identifying noises or isolates.

Deployment of a charging network based on a cost-benefit model and the CFSFDP algorithm

PurposeThis research aims to focus on the problem of optimizing the layout of charging stations.Design/methodology/approachThe authors estimate the total number of charging stations by the number of existing gas stations using clustering by fast search-and-find of density peaks (CFSFDP) algorithm.FindingsThis paper reveals the relationship between the distance and the population. Priority should be given to the short-distance and high-frequency travel charging needs in the city, followed by low frequency and travel, which can improve the use efficiency of the whole charging network.Originality/valueFirst, the total number of charging stations is estimated by the number of existing gas stations. This method can fully consider the growth potential of charging vehicles instead of the traditional cars. Besides, the cost-benefit method is proposed to explore the marginal coverage distance of the city’s charging network and can be used to reveal the relationship between the distance and the population.

Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric Decomposition

Unmanned aerial vehicles (UAV) have become vital targets in civilian and military fields. However, the polarization characteristics are rarely studied. This paper studies the polarization property of UAVs via the fusion of three polarimetric decomposition methods. A novel algorithm is presented to classify and recognize UAVs automatically which includes a clustering method proposed in “Science”, one of the top journals in academia. Firstly, the selection of the imaging algorithm ensures the quality of the radar images. Secondly, local geometrical structures of UAVs can be extracted based on Pauli, Krogager, and Cameron polarimetric decomposition. Finally, the proposed algorithm with clustering by fast search and find of density peaks (CFSFDP) has been demonstrated to be better than the original methods under the various noise conditions with the fusion of three polarimetric decomposition methods.

Clustering by defining and merging candidates of cluster centers via independence and affinity

Clustering analysis is to classify elements into categories based on their similarity. Clustering by fast search and find of density peaks (CFSFDP) has been proven to be an effective and novel algorithm, which identifies the centers of clusters with density maxima. However, the performance of CFSFDP is quite sensitive to the estimation of densities, that is exactly the selection of the cutoff distance (dc). In a conventional way, the selection of dc is based on subjective experience. It meets difficulties in finding an appropriate dc, especially for detecting nonspherical clusters, because CFSFDP cannot perform well when there are more than one density peak for one cluster. Besides, another barrier of applying CFSFDP is that manual interaction is always required for making an effective selection of cluster centers. In this paper, a new density-based clustering algorithm, clustering by defining and merging candidates of cluster centers via independence and affinity (CDMC-IA), is proposed. With its strategy, an appropriate value of cutoff distance dc can be well suggested and the robustness of the method itself is enhanced. Moreover, CDMC-IA introduces a new quantity independence to sort and select cluster centers, instead of human based selection from decision graph. Another quantity affinity is also introduced, which well handles multiple density peaks existing in one cluster and is able to assign each data point to the its targeted cluster. The performance of applying conventional clustering methods to benchmark datasets will be compared with the proposed method in this paper.

Relieve the congestion by shuttle bus in rush hours using aggregation clustering algorithm on group travel pattern

SummaryThe travel demand is of vital importance for transport planning. Especially in rush hours, the commuters aggregate in certain areas, resulting in traffic jams, which hinder the normal operation of the urban traffic. The key issue to solve the problem is to find out the demand of the passengers and accordingly arrange the transit resource more reasonably. This paper proposes a framework that provides a shuttle bus solution to satisfy the travel demand of the gathered passengers in rush hours according to their travel history obtained by smartcard data. Firstly, an aggregation algorithm basing on Clustering by fast search and find of density peaks (CFSFDP) is presented to highlight the area, which consists of adjacent bus stations with high passenger flow, addressed as spark region. Secondly, group travel pattern (GTP) is put forward to describe the travel trend on a city‐wide scale, which reveals the common travel demand of the commuters. Lastly, an algorithm named Variable Visibility Path Optimization Algorithm based on ant colony algorithm is proposed to make schedule solution of shuttle bus according to GTP basing on the historical running information of the bus. The experiment basing on the bus passenger flow data collected by AFCS in Aug 2014 from Beijing shows that our method helps to ease the traffic aggregation effectively and practically and offer reference to the bus scheduling issue.

Fast density clustering strategies based on the k-means algorithm

Clustering by fast search and find of density peaks (CFSFDP) is a state-of-the-art density-based clustering algorithm that can effectively find clusters with arbitrary shapes. However, it requires to calculate the distances between all the points in a data set to determine the density and separation of each point. Consequently, its computational cost is extremely high in the case of large-scale data sets. In this study, we investigate the application of the k-means algorithm, which is a fast clustering technique, to enhance the scalability of the CFSFDP algorithm while maintaining its clustering results as far as possible. Toward this end, we propose two strategies. First, based on concept approximation, an acceleration algorithm (CFSFDP+A) involving fewer distance calculations is proposed to obtain the same clustering results as those of the original algorithm. Second, to further expand the scalability of the original algorithm, an approximate algorithm (CFSFDP+DE) based on exemplar clustering is proposed to rapidly obtain approximate clustering results of the original algorithm. Finally, experiments are conducted to illustrate the effectiveness and scalability of the proposed algorithms on several synthetic and real data sets.