Density Clustering Algorithm Based on KD-Tree and Voting Rules

Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.

A novel fast classification filtering algorithm for LiDAR point clouds based on small grid density clustering

Clustering filtering is usually a practical method for light detection and ranging(LiDAR)point clouds filtering according to their characteristic attributes.However,the amount of point cloud data is extremely large in practice,making it impossible to cluster point clouds data directly,and the filtering error is also too large.Moreover,many existing filtering algorithms have poor classification results in discontinuous terrain.This article proposes a new fast classification filtering algorithm based on density clustering,which can solve the problem of point clouds classification in discontinuous terrain.Based on the spatial density of LiDAR point clouds,also the features of the ground object point clouds and the terrain point clouds,the point clouds are clustered firstly by their elevations,and then the plane point clouds are selected.Thus the number of samples and feature dimensions of data are reduced.Using the DBSCAN clustering filtering method,the original point clouds are finally divided into noise point clouds,ground object point clouds,and terrain point clouds.The experiment uses 15 sets of data samples provided by the International Society for Photogrammetry and Remote Sensing(ISPRS),and the results of the proposed algorithm are compared with the other eight classical filtering algorithms.Quantitative and qualitative analysis shows that the proposed algorithm has good applicability in urban areas and rural areas,and is significantly better than other classic filtering algorithms in discontinuous terrain,with a total error of about 10%.The results show that the proposed method is feasible and can be used in different terrains.

Encephalitis Detection from EEG Fuzzy Density-Based Clustering Model with Multiple Centroid

Encephalitis is a brain inflammation disease.Encephalitis can yield to seizures,motor disability,or some loss of vision or hearing.Sometimes,encepha-litis can be a life-threatening and proper diagnosis in an early stage is very crucial.Therefore,in this paper,we are proposing a deep learning model for computerized detection of Encephalitis from the electroencephalogram data(EEG).Also,we propose a Density-Based Clustering model to classify the distinctive waves of Encephalitis.Customary clustering models usually employ a computed single centroid virtual point to define the cluster configuration,but this single point does not contain adequate information.To precisely extract accurate inner structural data,a multiple centroids approach is employed and defined in this paper,which defines the cluster configuration by allocating weights to each state in the cluster.The multiple EEG view fuzzy learning approach incorporates data from every sin-gle view to enhance the model's clustering performance.Also a fuzzy Density-Based Clustering model with multiple centroids(FDBC)is presented.This model employs multiple real state centroids to define clusters using Partitioning Around Centroids algorithm.The Experimental results validate the medical importance of the proposed clustering model.

Adaptive Density-Based Spatial Clustering of Applications with Noise(ADBSCAN)for Clusters of Different Densities

Finding clusters based on density represents a significant class of clustering algorithms.These methods can discover clusters of various shapes and sizes.The most studied algorithm in this class is theDensity-Based Spatial Clustering of Applications with Noise(DBSCAN).It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects.It requires two input parameters:epsilon(fixed neighborhood radius)and MinPts(the lowest number of objects in epsilon).However,it can’t handle clusters of various densities since it uses a global value for epsilon.This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one.Only user input in the proposed method is the MinPts.Epsilon on the other hand,is computed automatically based on statistical information of the dataset.The proposed method finds the core distance for each object in the dataset,takes the average of these distances as the first value of epsilon,and finds the clusters satisfying this density level.The remaining unclustered objects will be clustered using a new value of epsilon that equals the average core distances of unclustered objects.This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset’s size.The proposed method requires MinPts only as an input parameter because epsilon is computed from data.Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results.Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them.The accuracy of the method ranges from 92%to 100%for the experimented datasets.

CLUSTERING OF DOA DATA IN RADAR PULSE BASED ON SOFM AND CDBW

Clustering is the main method of deinterleaving of radar pulse using multi-parameter.However,the problem in clustering of radar pulses lies in finding the right number of clusters.To solve this problem,a method is proposed based on Self-Organizing Feature Maps(SOFM) and Composed Density between and within clusters(CDbw).This method firstly extracts the feature of Direction Of Arrival(DOA) data by SOFM using the characteristic of DOA parameter,and then cluster of SOFM.Through computing the cluster validity index CDbw,the right number of clusters is found.The results of simulation show that the method is effective in sorting the data of DOA.

Effective approach for outdoor obstacle detection by clustering LIDAR data context

A method of environment mapping using laser-based light detection and ranging(LIDAR)is proposed in this paper.This method not only has a good detection performance in a wide range of detection angles,but also facilitates the detection of dynamic and hollowed-out obstacles.Essentially using this method,an improved clustering algorithm based on fast search and discovery of density peaks(CBFD)is presented to extract various obstacles in the environment map.By comparing with other cluster algorithms,CBFD can obtain a favorable number of clusterings automatically.Furthermore,the experiments show that CBFD is better and more robust in functionality and performance than the K-means and iterative self-organizing data analysis techniques algorithm(ISODATA).

An optimized cluster density matrix embedding theory

We propose an optimized cluster density matrix embedding theory(CDMET).It reduces the computational cost of CDMET with simpler bath states.And the result is as accurate as the original one.As a demonstration,we study the distant correlations of the Heisenberg J_(1)-J_(2)model on the square lattice.We find that the intermediate phase(0.43≤sssim J_(2)≤sssim 0.62)is divided into two parts.One part is a near-critical region(0.43≤J_(2)≤0.50).The other part is the plaquette valence bond solid(PVB)state(0.51≤J_(2)≤0.62).The spin correlations decay exponentially as a function of distance in the PVB.

A Study on Numerical Calculation Method of Small Cluster Density in Percolation Model

Percolation theory deals with the numbers and properties of the clusters formed in the different occupation probability. In this Paper, we study the calculation method of small clusters. We calcu-lated the small cluster density of 1, 2 and 3 in the percolation model with the exact method and the numerical method. The results of the two methods are very close, which can be verified by each other. We find that the cluster density of all three kinds of small clusters reaches the highest value when the occupation probability is between 0.1 and 0.2. It is very difficult to get the analytical formula for the exact method when the cluster area is relatively large (such as the area is more than 50), so we can get the density value of the cluster by numerical method. We find that the time required calculating the cluster density is proportional to the percolation area, which is indepen-dent of the cluster size and the occupation probability.

A Novel Method of Deinterleaving Radar Pulse Sequences Based on a Modified DBSCAN Algorithm

A modified DBSCAN algorithm is presented for deinterleaving of radar pulses in modern EW environments.A main characteristic of the proposed method is that using only time of arrival of pulses,the method can sort the pulses efficiently.Other PDW information such as rise time,carrier frequency,pulse width,modulation on pulse,fall time and direction of arrival are not required.To identify the valid PRIs in a set of interleaved pulses,an innovative modification of the DBSCAN algorithm is introduced which is accurate and easy to implement.The proposed method determines valid PRIs more accurately and neglects the spurious ones more efficiently as compared to the classical histogram based algorithms such as SDIF.Furthermore,without specifying any input parameter,the proposed method can deinterleave radar pulses while up to 30%jitter is present in the associated PRI.The accuracy and efficiency of the proposed method are verified by computer simulations and real data results.Experimental simulations are based on different real and operational scenarios where the presence of missing and spurious pulses are also considered.So,the simulation results can be of practical significance.

几种经典聚类算法的比较研究

本文选取K-means、FCM、Spectral Cluster、Density Peak Cluster四种经典聚类算法作为研究对象,从理论和实验两个角度对它们进行比较研究。首先,本文介绍了聚类的含义、准则及应用;其次,本文分别阐述了四种算法的原理,并从理论角度分析它们的异同;再次,本文在UCI数据集上对四种算法执行了对比实验,比较它们的聚类准确率;最后,根据理论分析和对比实验的结果,得出四种算法适应不同类型数据集的结论。

An Adaptive Anomaly Detection Algorithm Based on CFSFDP

CFSFDP(Clustering by fast search and find of density peak)is a simple and crisp density clustering algorithm.It does not only have the advantages of density clustering algorithm,but also can find the peak of cluster automatically.However,the lack of adaptability makes it difficult to apply in intrusion detection.The new input cannot be updated in time to the existing profiles,and rebuilding profiles would waste a lot of time and computation.Therefore,an adaptive anomaly detection algorithm based on CFSFDP is proposed in this paper.By analyzing the influence of new input on center,edge and discrete points,the adaptive problem mainly focuses on processing with the generation of new cluster by new input.The improved algorithm can integrate new input into the existing clustering without changing the original profiles.Meanwhile,the improved algorithm takes the advantage of multi-core parallel computing to deal with redundant computing.A large number of experiments on intrusion detection on Android platform and KDDCUP 1999 show that the improved algorithm can update the profiles adaptively without affecting the original detection performance.Compared with the other classical algorithms,the improved algorithm based on CFSFDP has the good basic performance and more room of improvement.

Estimating Mechanical Vibration Period Using Smartphones

Driven by a wide range of real-world applications,significant efforts have recently been made to explore facile vibration measurement.Traditional vibration inspection systems are normally sensed via accelerometers,laser displacement sensors or velocimeters,and most of them are neither non-intrusive nor wide-spread.This paper presents a novel solution based on acoustic waves of commercial mobile phones to inspect mechanical vibration.The core observation is that the Doppler effect occurs when acoustic waves pass through a vibrating object.The study leverages this opportunity to build a bridge between the Doppler frequency excursion and the vibrating frequency of objects.The solution of difference operation of the reassignment vector is used to make time-frequency domain images more readable.Finally,by processing time-frequency images,the system further accomplishes two reconstruction approaches to find out the energy concentration of acoustic signals respectively based on ridges and clustering.Simulation and real-life applications are employed to show the effectiveness and practicability of the proposed approaches.Our prototype system can inspect the vibration period with a relative error of 0.08%.Furthermore,this paper studies two practical cases in life to associate our measurement solution with the requirements of daily life.

A survey of density based clustering algorithms

Density based clustering algorithms(DBCLAs)rely on the notion of density to identify clusters of arbitrary shapes,sizes with varying densities.Existing surveys on DB-CLAs cover only a selected set of algorithms.These surveys fail to provide an extensive information about a variety of DBCLAs proposed till date including a taxonomy of the algorithms.In this paper we present a comprehensive survey of various DB-CLAS over last two decades along with their classification.We group the DBCLAs in each of the four categories:density definition,parameter sensitivity,execution mode and nature of*data and further divide them into various classes under each of these categories.In addition,we compare the DBCLAs through their common features and variations in citation and conceptual dependencies.We identify various application areas of DBCLAS in domains such as astronomy,earth sciences,molecular biology,geography,multimedia.Our survey also identifies probable future directions of DBCLAs where involvement of density based methods may lead to favorable results.

Radar false alarm plots elimination based on multi-feature extraction and classification

Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination method based on multi-feature extraction and classification is proposed to effectively eliminate false alarm plots.Firstly,the density based spatial clustering of applications with noise(DBSCAN)algorithm is used to cluster the radar echo data processed by constant false-alarm rate(CFAR).The multi-features including the scale features,time domain features and transform domain features are extracted.Secondly,a feature evaluation method combining pearson correlation coefficient(PCC)and entropy weight method(EWM)is proposed to evaluate interrelation among features,effective feature combination sets are selected as inputs of the classifier.Finally,False alarm plots classified as clutters are eliminated.The experimental results show that proposed method can eliminate about 90%false alarm plots with less target loss rate.

A Health State Prediction Model Based on Belief Rule Base and LSTM for Complex Systems

In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,the Density Peak Clustering(DPC)algorithmis used todetermine referential values of indicators for BRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.

Identification and characterization of irregular consumptions of load data

The historical information of loadings on substation helps in evaluation of size of photovoltaic(PV)generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumption data, is proposed to separate the unusual consumption and to form the clusters of similar regular consumption. The method does optimal partition of the load pattern data into core points and border points, high and less dense regions, respectively. The local outlier factor, which does not require fixed probability distribution of data and statistical measures, ranks the unusual consumptions on only the border points, which are a few percent of the complete data. The suggested method finds the optimal or close to optimal number of clusters of similar shape of load patterns to detect regular peak and valley load demands on different days. Furthermore,identification and characterization of features pertaining to unusual consumptions in load pattern data have been done on border points only. The effectiveness of the proposed method and characterization is tested on two practical distribution systems.

Response of spatial structure of cotton root to soil-wetting patterns under mulched drip irrigation

The matching relationship between the spatial structure of cotton cluster root systems and soil-wetting patterns under mulched drip irrigation forms the theoretical basis for the technical design of mulched drip irrigation.A 2-year field experiment was conducted,in which different soil-wetting patterns were produced by setting different emitter discharge rates.The envelopes of cotton cluster root length densities were derived using the topological methodology and used to examine the effects of different soil-wetting patterns on the spatial structure of root systems and water uptake capacity within row spaces.The results showed that the root systems in rows of cotton grown under narrower and deeper soil-wetting patterns exhibited a single-peak distribution,while those under wider and shallower soil-wetting patterns exhibited a two-peak distribution.Furthermore,cotton rows grown near mulch edges experienced lower moisture stress,and wider and shallower soil-wetting patterns contributed to greater root growth rates in the vertical direction and resulted in more even potential water uptake capacities.The findings of this study revealed that wider and shallower soil-wetting patterns were more desirable for mulched drip irrigation of cotton and should be considered in the technical design of drip irrigation systems.