A Shared Natural Neighbors Based-Hierarchical Clustering Algorithm for Discovering Arbitrary-Shaped Clusters

In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.

A Multi-Token Sector Antenna Neighbor Discovery Protocol for Directional Ad Hoc Networks

In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work involves maintaining multiple tokens across the network.To prevent mutual interference among multi-token holders,we introduce the time and space non-interference theorems.Furthermore,we propose a master-slave strategy between tokens.When the master token holder(MTH)performs the neighbor discovery,it decides which 1-hop neighbor is the next MTH and which 2-hop neighbors can be the new slave token holders(STHs).Using this approach,the MTH and multiple STHs can simultaneously discover their neighbors without causing interference with each other.Building on this foundation,we provide a comprehensive procedure for the M-SAND protocol.We also conduct theoretical analyses on the maximum number of STHs and the lower bound of multi-token generation probability.Finally,simulation results demonstrate the time efficiency of the M-SAND protocol.When compared to the QSAND protocol,which uses only one token,the total neighbor discovery time is reduced by 28% when 6beams and 112 nodes are employed.

The GSI and Building a Neighborhood Community with a Shared Future

Against the backdrop of the international political and economic configuration featuring that“the East is rising and the West is declining”,relations between China and its neighboring countries enjoy steady development in general.However,the return of the Cold War mentality,rampant protectionism and prominent security governance issues have seriously threatened peace and stability in China’s neighboring region.

Sensing and Communication Integrated Fast Neighbor Discovery for UAV Networks

In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communication(ISAC),as an emerging technology in 6G mobile networks,has shown great potential in improving communication performance with the assistance of sensing information.ISAC obtains the prior information about node distribution,reducing the ND time.However,the prior information obtained through ISAC may be imperfect.Hence,an ND algorithm based on reinforcement learning is proposed.The learning automaton(LA)is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms.Besides,an efficient ND algorithm in the neighbor maintenance phase is designed,which applies the Kalman filter to predict node movement.Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32%compared with the Scan-Based Algorithm(SBA),which proves the efficiency of the proposed ND algorithms.

Probability Distribution of Arithmetic Average of China Aviation Network Edge Vertices Nearest Neighbor Average Degree Value and Its Evolutionary Trace Based on Complex Network

In order to reveal the complex network characteristics and evolution principle of China aviation network,the probability distribution and evolution trace of arithmetic average of edge vertices nearest neighbor average degree values of China aviation network were studied based on the statistics data of China civil aviation network in 1988,1994,2001,2008 and 2015.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the route between cities as the edge of the network.Based on the statistical data,the arithmetic averages of edge vertices nearest neighbor average degree values of China aviation network in 1988,1994,2001,2008 and 2015 were calculated.Using the probability statistical analysis method,it was found that the arithmetic average of edge vertices nearest neighbor average degree values had the probability distribution of normal function and the position parameters and scale parameters of the probability distribution had linear evolution trace.

基于K-Nearest Neighbor和神经网络的糖尿病分类研究

为实现糖尿病的早期筛查,提高对糖尿病分类的准确度,在研究有关糖尿病危险因素的基础上,增加糖化血红蛋白作为糖尿病早期筛查的特征之一。研究中选取与人类最为相似的食蟹猴作为研究对象,利用年龄、血压、腹围、BMI、糖化血红蛋白以及空腹血糖作为特征输入,将正常、糖尿病前期和糖尿病作为类别输出,利用K-Nearest Neighbor(KNN)和神经网络两种方法对其分类。发现在增加糖化血红蛋白作为分类特征之一时,KNN(K=3)和神经网络的分类准确率分别为81.8%和92.6%,明显高于没有这一特征时的准确率(68.1%和89.7%),KNN和神经网络都可以对食蟹猴数据进行分类和识别,起到早期筛查作用。

一种基于特征加权的K Nearest Neighbor算法

传统的KNN算法一般采用欧式距离公式度量两样本间的距离.由于在实际样本数据集合中每一个属性对样本的贡献作用是不尽相同的,通常采用加权欧式距离公式.笔者提出一种计算权重的方法,即基于特征加权KNN算法.经实验证明,该算法与经典的赋权算法相比具有较好的分类效果.

内容分发协议ShareStorm的Neighbor Selection机制

针对BitTorrent行为及其Neighbor Selection机制存在的缺陷,提出一种有效度量P2P内容分发协议的实验方法,并设计一种新的内容分发协议ShareStorm,该协议的Neighbor Selection机制充分考虑互联网拓扑和通讯成本,更倾向于与本地的peers建立邻居关系.经初步验证,在下载完成时间这个最主要的性能指标上,Share-Storm比BitTorrent至少减少50%.

IPv6中Neighbor Discovery协议及其测试

IPv6协议是下一代互联网协议,NeighborDiscovery(邻居发现)协议是IPv6协议的一个重要组成部分,RFC2461是邻居发现协议的标准文本。为了检查各个生产厂家的实现是否与标准文本相一致,文章提出了一种协议一致性测试的方法,并开发了一个进行IPv6一致性测试的系统。该文提出了逻辑测试结构和虚拟测试器的概念,并在这个测试系统中应用了这两种测试结构和概念,还给出了一个邻居发现协议的测试实例。最后,给出了笔者对一种UNIX系统上实现的邻居发现协议进行一致性测试得到的结果,并对其与标准文本不一致的部分进行了分析说明。

基于机器学习的冠心病风险预测模型构建与比较

背景冠状动脉粥样硬化性心脏病(以下简称冠心病)是全球重要的死亡原因之一。目前关于冠心病风险评估的研究在逐年增长。然而,在这些研究中常忽略了数据不平衡的问题,而解决该问题对于提高分类算法中识别冠心病风险的准确性至关重要。目的探索冠心病的影响因素,通过使用2种平衡数据的方法,基于5种算法建立冠心病风险相关的预测模型,比较这5种模型对冠心病风险的预测价值。方法基于2021年美国国家行为风险因素监测系统(BRFSS)横断面调查数据筛选出112606名研究对象的健康相关风险行为、慢性健康状况等24个变量信息,结局指标为自我报告是否患有冠心病并据此分为冠心病组和非冠心病组。通过进行单因素分析和逐步Logistic回归分析探索冠心病发生的影响因素并筛选出纳入预测模型的变量。随机抽取112606名受访者的10%(共计11261名),以8∶2的比例随机划分为训练与测试的数据集,采用随机过采样和合成少数过采样技术(SMOTE)两种过采样的方法处理不平衡数据,基于k最邻近算法(KNN)、Logistic回归、支持向量机(SVM)、决策树和XGBoost算法分别建立冠心病预测模型。结果两组年龄、性别、BMI、种族、婚姻状态、教育水平、收入水平、家里有几个孩子、是否被告知患高血压、是否被告知处于高血压前期、是否被告知患妊娠高血压、现在是否在服用高血压药物、是否被告知患有高脂血症、是否被告知患有糖尿病、吸烟情况、过去30 d内是否至少喝过1次酒、是否为重度饮酒者、是否为酗酒者、过去30 d内是否有体育锻炼、心理健康状况以及自我健康评价比较,差异有统计学意义(P<0.05)。逐步Logistic回归分析结果显示:年龄、性别、BMI、种族、教育水平、收入水平、是否被告知患高血压、是否被告知处于高血压前期、是否被告知患妊娠高血压、现在是否在服用高血压药物、是否被告知患有高脂血症、是否被告知患有糖尿病、吸烟情况、过去30 d内是否至少喝过1次酒、是否为重度饮酒者、是否为酗酒者以及自我健康评价为冠心病的影响因素(P<0.05)。风险模型构建的分析结果显示:k最邻近算法、Logistic回归、支持向量机、决策树和XGBoost采用SMOTE处理不平衡数据的总体分类精度分别为59.2%、67.4%、66.2%、69.2%和85.9%,召回率分别为75.2%、71.4%、70.5%、62.9%和34.8%,精确度分别为15.4%、18.2%、17.5%、17.6%和28.7%,F值分别为0.256、0.290、0.280、0.275和0.315,受试者工作特征曲线下面积分别为0.80、0.78、0.72、0.72和0.82;采用随机过采样处理不平衡数据的总体分类精度分别为62.5%、68.5%、69.0%、60.2%和70.1%,召回率分别为70.0%、69.5%、71.9%、69.0%和67.6%;精确度分别为15.8%、18.4%、19.1%、14.8%和19.0%,F值分别为0.258、0.291、0.302、0.244和0.297,受试者工作特征曲线下面积分别为0.80、0.77、0.72、0.72和0.83。结论本研究不仅确认了已知冠心病的影响因素,还发现了自我健康评价水平、收入水平和教育水平对冠心病具有潜在影响。在使用2种数据平衡方法后,5种算法的性能显著提高。其中XGBoost模型表现最佳,可作为未来优化冠心病预测模型的参考。此外,鉴于XGBoost模型的优异性能以及逐步Logistic回归的操作便捷和可解释性,推荐在冠心病风险预测模型中结合使用数据平衡后的XGBoost和逐步Logistic回归分析。

基于不规则区域划分方法的k-Nearest Neighbor查询算法

随着越来越多的数据累积,对数据处理能力和分析能力的要求也越来越高.传统k-Nearest Neighbor(k NN)查询算法由于其容易导致计算负载整体不均衡的规则区域划分方法及其单个进程或单台计算机运行环境的较低数据处理能力.本文提出并详细介绍了一种基于不规则区域划分方法的改进型k NN查询算法,并利用对大规模数据集进行分布式并行计算的模型Map Reduce对该算法加以实现.实验结果与分析表明,Map Reduce框架下基于不规则区域划分方法的k NN查询算法可以获得较高的数据处理效率,并可以较好的支持大数据环境下数据的高效查询.

Rebirth,the Pursuit of "My Neighbor Rosicky"

"My Neighbor Rosicky,"one of the representative short stories by Willa Cather,the American local colorist famous for her de scription of frontier life on the Great Plains,conveys Cather's deep understanding about life.In this paper,the author attempts to apply Jung's archetypal theory to the analysis of Mr.Rosicky,the hero of the short story and gets the conclusion that Mr.Rosicky is in constant pursuit of"Rebirth"through the medium,"the ground."

Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico

As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring strategies using geospatial technology.Among statistical methods for mapping biomass,there is a nonparametric approach called k-nearest neighbor(kNN).We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone.Satellite derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the purpose.Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique.The results indicate that the Most Similar Neighbor(MSN)approach maximizes the correlation between predictor and response variables(r=0.9).Our results are in agreement with those reported in the literature.These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation(REDD+).

Support Vector Machine-Based Fault Diagnosis of Power Transformer Using k Nearest-Neighbor Imputed DGA Dataset

Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation method with two different distance metrics: Euclidean and Cityblock. Thereafter, using these imputed datasets as inputs, this study applies Support Vector Machine (SVM) to built models which are used to classify transformer faults. Experimental results are provided to show the effectiveness of the proposed approach.

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.

Pruned fuzzy K-nearest neighbor classifier for beat classification

Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.

A Short-Term Traffic Flow Forecasting Method Based on a Three-Layer K-Nearest Neighbor Non-Parametric Regression Algorithm

Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting method based on a three-layer K-nearest neighbor non-parametric regression algorithm is proposed. Specifically, two screening layers based on shape similarity were introduced in K-nearest neighbor non-parametric regression method, and the forecasting results were output using the weighted averaging on the reciprocal values of the shape similarity distances and the most-similar-point distance adjustment method. According to the experimental results, the proposed algorithm has improved the predictive ability of the traditional K-nearest neighbor non-parametric regression method, and greatly enhanced the accuracy and real-time performance of short-term traffic flow forecasting.

Face Recognition Based on Support Vector Machine and Nearest Neighbor Classifier

Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-

Cultural Adaptation in Tim Winton's "Neighbors"

As the most productive and prestigious writer in contemporary Australian literature,Tim Winton is noted not only for his novels but also for his short stories.Neighbors is a case in point.The short story describes the daily trivial incidents in the multi-cultural background according to the line of a newly-weds moving to a new neighborhood.The young couple gradually understood and communicated with their neighbors and eventually achieved cultural adaptation.

A Proximity-Aware BitTorrent System via Tracker-Side Biased Neighbor Selection

To address cross-ISP traffic problem caused by BitTorrent,we present our design and evaluation of a proximity-aware BitTorrent system. In our approach,clients generate global proximity-aware information by using landmark clustering;the tracker uses this proximity to maintain all peers in an orderly way and hands back a biased subset consisting of the peers who are physically closest to the requestor. Our approach requires no co-operation between P2P users and their Internet infra structures,such as ISPs or CDNs,no constantly path monitoring or probing their neighbors. The simulation results show that our approach can not only reduce unnecessary cross-ISP traffic,but also allow downloadsing fast.