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Comprehensive K-Means Clustering
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作者 Ethan Xiao 《Journal of Computer and Communications》 2024年第3期146-159,共14页
The k-means algorithm is a popular data clustering technique due to its speed and simplicity. However, it is susceptible to issues such as sensitivity to the chosen seeds, and inaccurate clusters due to poor initial s... The k-means algorithm is a popular data clustering technique due to its speed and simplicity. However, it is susceptible to issues such as sensitivity to the chosen seeds, and inaccurate clusters due to poor initial seeds, particularly in complex datasets or datasets with non-spherical clusters. In this paper, a Comprehensive K-Means Clustering algorithm is presented, in which multiple trials of k-means are performed on a given dataset. The clustering results from each trial are transformed into a five-dimensional data point, containing the scope values of the x and y coordinates of the clusters along with the number of points within that cluster. A graph is then generated displaying the configuration of these points using Principal Component Analysis (PCA), from which we can observe and determine the common clustering patterns in the dataset. The robustness and strength of these patterns are then examined by observing the variance of the results of each trial, wherein a different subset of the data keeping a certain percentage of original data points is clustered. By aggregating information from multiple trials, we can distinguish clusters that consistently emerge across different runs from those that are more sensitive or unlikely, hence deriving more reliable conclusions about the underlying structure of complex datasets. Our experiments show that our algorithm is able to find the most common associations between different dimensions of data over multiple trials, often more accurately than other algorithms, as well as measure stability of these clusters, an ability that other k-means algorithms lack. 展开更多
关键词 k-means clustering
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Hybrid Genetic Algorithm with K-Means for Clustering Problems 被引量:1
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作者 Ahamed Al Malki Mohamed M. Rizk +1 位作者 M. A. El-Shorbagy A. A. Mousa 《Open Journal of Optimization》 2016年第2期71-83,共14页
The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty c... The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty clusters depending on initial center vectors. Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary principles of natural selection and genetics. This paper presents a hybrid version of the k-means algorithm with GAs that efficiently eliminates this empty cluster problem. Results of simulation experiments using several data sets prove our claim. 展开更多
关键词 cluster Analysis genetic Algorithm k-means
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Investigation of the J-TEXT plasma events by k-means clustering algorithm 被引量:1
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作者 李建超 张晓卿 +11 位作者 张昱 Abba Alhaji BALA 柳惠平 周帼红 王能超 李达 陈忠勇 杨州军 陈志鹏 董蛟龙 丁永华 the J-TEXT Team 《Plasma Science and Technology》 SCIE EI CAS CSCD 2023年第8期38-43,共6页
Various types of plasma events emerge in specific parameter ranges and exhibit similar characteristics in diagnostic signals,which can be applied to identify these events.A semisupervised machine learning algorithm,th... Various types of plasma events emerge in specific parameter ranges and exhibit similar characteristics in diagnostic signals,which can be applied to identify these events.A semisupervised machine learning algorithm,the k-means clustering algorithm,is utilized to investigate and identify plasma events in the J-TEXT plasma.This method can cluster diverse plasma events with homogeneous features,and then these events can be identified if given few manually labeled examples based on physical understanding.A survey of clustered events reveals that the k-means algorithm can make plasma events(rotating tearing mode,sawtooth oscillations,and locked mode)gathering in Euclidean space composed of multi-dimensional diagnostic data,like soft x-ray emission intensity,edge toroidal rotation velocity,the Mirnov signal amplitude and so on.Based on the cluster analysis results,an approximate analytical model is proposed to rapidly identify plasma events in the J-TEXT plasma.The cluster analysis method is conducive to data markers of massive diagnostic data. 展开更多
关键词 k-means cluster analysis plasma event machine learning
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Genetic Algorithm Combined with the K-Means Algorithm:A Hybrid Technique for Unsupervised Feature Selection
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作者 Hachemi Bennaceur Meznah Almutairy Norah Alhussain 《Intelligent Automation & Soft Computing》 SCIE 2023年第9期2687-2706,共20页
The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational costs.The literature inclu... The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational costs.The literature includes much research on feature selection for supervised learning.However,feature selection for unsupervised learning has only recently been studied.Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate.This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS,which combines the genetic algorithm(GA)approach with the classical k-Means algorithm.In the proposed algorithm,a new fitness func-tion is designed in addition to new smart crossover and mutation operators.The effectiveness of this algorithm is demonstrated on various datasets.Fur-thermore,the performance of GAk-MEANS has been compared with other genetic algorithms,such as the genetic algorithm using the Sammon Error Function and the genetic algorithm using the Sum of Squared Error Function.Additionally,the performance of GAk-MEANS is compared with the state-of-the-art statistical unsupervised feature selection techniques.Experimental results show that GAk-MEANS consistently selects subsets of features that result in better classification accuracy compared to others.In particular,GAk-MEANS is able to significantly reduce the size of the subset of selected features by an average of 86.35%(72%–96.14%),which leads to an increase of the accuracy by an average of 3.78%(1.05%–6.32%)compared to using all features.When compared with the genetic algorithm using the Sammon Error Function,GAk-MEANS is able to reduce the size of the subset of selected features by 41.29%on average,improve the accuracy by 5.37%,and reduce the time by 70.71%.When compared with the genetic algorithm using the Sum of Squared Error Function,GAk-MEANS on average is able to reduce the size of the subset of selected features by 15.91%,and improve the accuracy by 9.81%,but the time is increased by a factor of 3.When compared with the machine-learning based methods,we observed that GAk-MEANS is able to increase the accuracy by 13.67%on average with an 88.76%average increase in time. 展开更多
关键词 genetic algorithm unsupervised feature selection k-means clustering
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Quantitative Method of Classification and Discrimination of a Porous Carbonate Reservoir Integrating K-means Clustering and Bayesian Theory
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作者 FANG Xinxin ZHU Guotao +2 位作者 YANG Yiming LI Fengling FENG Hong 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 2023年第1期176-189,共14页
Reservoir classification is a key link in reservoir evaluation.However,traditional manual means are inefficient,subjective,and classification standards are not uniform.Therefore,taking the Mishrif Formation of the Wes... Reservoir classification is a key link in reservoir evaluation.However,traditional manual means are inefficient,subjective,and classification standards are not uniform.Therefore,taking the Mishrif Formation of the Western Iraq as an example,a new reservoir classification and discrimination method is established by using the K-means clustering method and the Bayesian discrimination method.These methods are applied to non-cored wells to calculate the discrimination accuracy of the reservoir type,and thus the main reasons for low accuracy of reservoir discrimination are clarified.The results show that the discrimination accuracy of reservoir type based on K-means clustering and Bayesian stepwise discrimination is strongly related to the accuracy of the core data.The discrimination accuracy rate of TypeⅠ,TypeⅡ,and TypeⅤreservoirs is found to be significantly higher than that of TypeⅢand TypeⅣreservoirs using the method of combining K-means clustering and Bayesian theory based on logging data.Although the recognition accuracy of the new methodology for the TypeⅣreservoir is low,with average accuracy the new method has reached more than 82%in the entire study area,which lays a good foundation for rapid and accurate discrimination of reservoir types and the fine evaluation of a reservoir. 展开更多
关键词 UPSTREAM resource exploration reservoir classification CARBONATE k-means clustering Bayesian discrimination CENOMANIAN-TURONIAN Iraq
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Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation
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作者 Mona Jamjoom Ahmed Elhadad +1 位作者 Hussein Abulkasim Safia Abbas 《Computers, Materials & Continua》 SCIE EI 2023年第7期367-382,共16页
Several pests feed on leaves,stems,bases,and the entire plant,causing plant illnesses.As a result,it is vital to identify and eliminate the disease before causing any damage to plants.Manually detecting plant disease ... Several pests feed on leaves,stems,bases,and the entire plant,causing plant illnesses.As a result,it is vital to identify and eliminate the disease before causing any damage to plants.Manually detecting plant disease and treating it is pretty challenging in this period.Image processing is employed to detect plant disease since it requires much effort and an extended processing period.The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases,including Phytophthora infestans,Fusarium graminearum,Puccinia graminis,tomato yellow leaf curl.Therefore,this work uses the Support vector machine(SVM)classifier to detect and classify the plant disease using various steps like image acquisition,Pre-processing,Segmentation,feature extraction,and classification.The gray level co-occurrence matrix(GLCM)and the local binary pattern features(LBP)are used to identify the disease-affected portion of the plant leaf.According to experimental data,the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy. 展开更多
关键词 SVM machine learning GLCM algorithm k-means clustering LBP
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Clustering Countries on COVID-19 Data among Different Waves Using K-Means Clustering
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作者 Muhtasim   Md. Abdul Masud 《Journal of Computer and Communications》 2023年第7期1-14,共14页
The COVID-19 pandemic has caused an unprecedented spike in confirmed cases in 230 countries globally. In this work, a set of data from the COVID-19 coronavirus outbreak has been subjected to two well-known unsupervise... The COVID-19 pandemic has caused an unprecedented spike in confirmed cases in 230 countries globally. In this work, a set of data from the COVID-19 coronavirus outbreak has been subjected to two well-known unsupervised learning techniques: K-means clustering and correlation. The COVID-19 virus has infected several nations, and K-means automatically looks for undiscovered clusters of those infections. To examine the spread of COVID-19 before a vaccine becomes widely available, this work has used unsupervised approaches to identify the crucial county-level confirmed cases, death cases, recover cases, total_cases_per_million, and total_deaths_per_million aspects of county-level variables. We combined countries into significant clusters using this feature subspace to assist more in-depth disease analysis efforts. As a result, we used a clustering technique to examine various trends in COVID-19 incidence and mortality across nations. This technique took the key components of a trajectory and incorporates them into a K-means clustering process. We separated the trend lines into measures that characterize various features of a trend. The measurements were first reduced in dimension, then clustered using a K-means algorithm. This method was used to individually calculate the incidence and death rates and then compare them. 展开更多
关键词 COVID-19 Epidemic k-means clustering CORRELATIONS Infection Control SARS-CoV-2 Time Series
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基于改进K-means聚类和遗传算法的混合算法求解异构车辆路径问题
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作者 吴麟麟 吕一鸣 +1 位作者 何美玲 韩珣 《物流技术》 2024年第7期48-62,共15页
由于目前单一车型配送存在资源浪费和效率低下等问题,选取确定数量的不同车型对各客户点进行配送服务往往可以得到更优的配送路径方案。针对这一点,描述了一种异构车辆路径问题,并建立了具有固定车辆数且考虑固定成本、可变成本以及时... 由于目前单一车型配送存在资源浪费和效率低下等问题,选取确定数量的不同车型对各客户点进行配送服务往往可以得到更优的配送路径方案。针对这一点,描述了一种异构车辆路径问题,并建立了具有固定车辆数且考虑固定成本、可变成本以及时间窗惩罚成本的混合整数规划模型。同时,提出了一种基于改进K-means聚类和遗传算法的混合算法对模型进行求解。实验仿真先求解不考虑时间窗的问题初步证明混合算法的有效性,再在带时间窗的问题中求解不同规模算例的单一及异构车型结果,以证明异构车型配送更优。最后,对该混合算法的求解结果与其他混合算法的求解结果进行对比分析,证明了混合算法的优越性。研究结果表明:该混合算法求解的异构车型结果优于单一车型,并且比其他混合算法求解的异构车型结果更优,异构车辆配送使用的配送车辆数更少,总成本也更低,该混合算法具有更好的效率和性能。 展开更多
关键词 异构车辆路径问题 改进k-means聚类算法 遗传算法 混合算法
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Improved k-means clustering algorithm 被引量:16
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作者 夏士雄 李文超 +2 位作者 周勇 张磊 牛强 《Journal of Southeast University(English Edition)》 EI CAS 2007年第3期435-438,共4页
In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering a... In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering algorithm is proposed. First, the concept of a silhouette coefficient is introduced, and the optimal clustering number Kopt of a data set with unknown class information is confirmed by calculating the silhouette coefficient of objects in clusters under different K values. Then the distribution of the data set is obtained through hierarchical clustering and the initial clustering-centers are confirmed. Finally, the clustering is completed by the traditional k-means clustering. By the theoretical analysis, it is proved that the improved k-means clustering algorithm has proper computational complexity. The experimental results of IRIS testing data set show that the algorithm can distinguish different clusters reasonably and recognize the outliers efficiently, and the entropy generated by the algorithm is lower. 展开更多
关键词 clustering k-means algorithm silhouette coefficient
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基于BBO优化K-means算法的WSN分簇路由算法 被引量:1
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作者 彭程 谭冲 +1 位作者 刘洪 郑敏 《中国科学院大学学报(中英文)》 CAS CSCD 北大核心 2024年第3期357-364,共8页
针对无线传感器网络中传感器节点能量有限、网络生存期短的问题,提出一种基于生物地理学算法优化K-means的无线传感器网络分簇路由算法BBOK-GA。成簇阶段,通过生物地理学优化算法改进K-means算法,避免求解时陷入局部最优。根据能量因子... 针对无线传感器网络中传感器节点能量有限、网络生存期短的问题,提出一种基于生物地理学算法优化K-means的无线传感器网络分簇路由算法BBOK-GA。成簇阶段,通过生物地理学优化算法改进K-means算法,避免求解时陷入局部最优。根据能量因子和距离因子设计了新的适应度函数选举最优簇首,完成分簇任务。数据传输阶段,则利用遗传算法为簇首节点搜寻到基站的最佳数据传输路径。仿真结果表明,相较于LEACH、LEACH-C、K-GA等算法,BBOK-GA降低了网络能耗,提高了网络吞吐量,延长了网络生存周期。 展开更多
关键词 无线传感器网络 生物地理学优化算法 遗传算法 k-means算法 分簇路由
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Genetic Diversity and Clustering Analysis of 48Cultivars of Olea euyopaea L. 被引量:1
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作者 宁德鲁 陈少瑜 +4 位作者 陈海云 李瑞 李勇杰 毛云玲 吴涛 《Agricultural Science & Technology》 CAS 2013年第9期1215-1219,共5页
Inter-simple sequence repeat(ISSR) molecular markers were applied to analyze the genetic diversity and clustering of 48 introduced and bred cultivars of Olea euyopaea L. Totally 106 DNA bands were amplified by 11 sc... Inter-simple sequence repeat(ISSR) molecular markers were applied to analyze the genetic diversity and clustering of 48 introduced and bred cultivars of Olea euyopaea L. Totally 106 DNA bands were amplified by 11 screened primers, including 99 polymorphic bands; the percentage of polymorphic loci was 93.40%, indicating a rich genetic diversity in Olea euyopaea L. germplasm resources. Based on Nei's genetic distances between various cultivars, a dendrogram of 48 cultivars of Olea euyopaea L. was constructed using unweighted pair-group(UPMGA)method,which showed that 48 cultivars were clustered into four main categories; 84.6% of native cultivars were clustered into two categories; most of introduced cultivars were clustered based on their sources and main usages but not on their geographic origins. This study will provide references for the utilization and further genetic improvement of Olea euyopaea L. germplasm resources. 展开更多
关键词 Olea euyopaea L. genetic diversity clustering analysis
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基于K-means聚类和极限学习机组合算法的短期光伏功率预测 被引量:1
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作者 黄牧涛 邢芳菲 +1 位作者 陈兴邦 卢明 《水电能源科学》 北大核心 2024年第2期217-220,216,共5页
考虑光伏功率的预测精度强依赖于天气模态和气候条件等因素影响,提出了基于极限学习机组合算法的短期光伏功率预测方法。首先,基于K-means聚类算法进行天气分型,分为4个季节下晴天、多云天气、阴雨天气共12组不同天气类别。其次,针对天... 考虑光伏功率的预测精度强依赖于天气模态和气候条件等因素影响,提出了基于极限学习机组合算法的短期光伏功率预测方法。首先,基于K-means聚类算法进行天气分型,分为4个季节下晴天、多云天气、阴雨天气共12组不同天气类别。其次,针对天气分型结果,基于极限学习机ELM、遗传算法改进的极限学习机GA-ELM、鸟群算法改进的极限学习机BSA-ELM3种算法构建光伏功率预测模型。最后,以某光伏电站数据进行所提模型验证。预测结果表明,BSA-ELM预测精度最高,12种天气预测精度达到90%左右,各季节中预测精度最高的天气类型均为晴天,多云天气精度高于阴雨天气精度,可为含高比例光伏并网的新型电力系统安全稳定运行提供有效数据支撑。 展开更多
关键词 光伏发电功率预测 k-means聚类 天气分型 极限学习机算法 遗传算法 鸟群算法
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Decoding the genetic landscape of autism:A comprehensive review
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作者 Mohammed Al-Beltagi Nermin Kamal Saeed +2 位作者 Adel Salah Bediwy Eman A Bediwy Reem Elbeltagi 《World Journal of Clinical Pediatrics》 2024年第3期98-136,共39页
BACKGROUND Autism spectrum disorder(ASD)is a complex neurodevelopmental condition characterized by heterogeneous symptoms and genetic underpinnings.Recent advancements in genetic and epigenetic research have provided ... BACKGROUND Autism spectrum disorder(ASD)is a complex neurodevelopmental condition characterized by heterogeneous symptoms and genetic underpinnings.Recent advancements in genetic and epigenetic research have provided insights into the intricate mechanisms contributing to ASD,influencing both diagnosis and therapeutic strategies.AIM To explore the genetic architecture of ASD,elucidate mechanistic insights into genetic mutations,and examine gene-environment interactions.METHODS A comprehensive systematic review was conducted,integrating findings from studies on genetic variations,epigenetic mechanisms(such as DNA methylation and histone modifications),and emerging technologies[including Clustered Regularly Interspaced Short Palindromic Repeats(CRISPR)-Cas9 and single-cell RNA sequencing].Relevant articles were identified through systematic searches of databases such as PubMed and Google Scholar.RESULTS Genetic studies have identified numerous risk genes and mutations associated with ASD,yet many cases remain unexplained by known factors,suggesting undiscovered genetic components.Mechanistic insights into how these genetic mutations impact neural development and brain connectivity are still evolving.Epigenetic modifications,particularly DNA methylation and non-coding RNAs,also play significant roles in ASD pathogenesis.Emerging technologies like CRISPR-Cas9 and advanced bioinformatics are advancing our understanding by enabling precise genetic editing and analysis of complex genomic data.CONCLUSION Continued research into the genetic and epigenetic underpinnings of ASD is crucial for developing personalized and effective treatments.Collaborative efforts integrating multidisciplinary expertise and international collaborations are essential to address the complexity of ASD and translate genetic discoveries into clinical practice.Addressing unresolved questions and ethical considerations surrounding genetic research will pave the way for improved diagnostic tools and targeted therapies,ultimately enhancing outcomes for individuals affected by ASD. 展开更多
关键词 Autism spectrum disorder geneticS EPIgeneticS clustered Regularly Interspaced Short Palindromic Repeats-Cas9 Gene-environment interactions Personalized medicine
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Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters
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作者 Byoungwook Kim Hong-Jun Jang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第5期1275-1294,共20页
As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is increasing.One of the famous algorithms ... As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is increasing.One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical data.In this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN algorithm.The existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the algorithm.To solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each keyword.In order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in groups.We conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force method.The experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 times.GKM-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB). 展开更多
关键词 Spatial clustering DBSCAN algorithm genetic algorithm textual information
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Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means
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作者 Bo-Han Wu Ran-Hong Xie +3 位作者 Li-Zhi Xiao Jiang-Feng Guo Guo-Wen Jin Jian-Wei Fu 《Petroleum Science》 SCIE EI CSCD 2023年第5期2747-2758,共12页
In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tig... In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method. 展开更多
关键词 Tight sandstone Integrated reservoir classification Principal component analysis Simulated annealing genetic algorithm Fuzzy cluster means
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An efficient enhanced k-means clustering algorithm 被引量:30
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作者 FAHIM A.M SALEM A.M +1 位作者 TORKEY F.A RAMADAN M.A 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2006年第10期1626-1633,共8页
In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared dista... In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this paper, we present a simple and efficient clustering algorithm based on the k-means algorithm, which we call enhanced k-means algorithm. This algorithm is easy to implement, requiring a simple data structure to keep some information in each iteration to be used in the next iteration. Our experimental results demonstrated that our scheme can improve the computational speed of the k-means algorithm by the magnitude in the total number of distance calculations and the overall time of computation. 展开更多
关键词 clustering algorithms cluster analysis k-means algorithm Data analysis
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Improved method for the feature extraction of laser scanner using genetic clustering 被引量:6
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作者 Yu Jinxia Cai Zixing Duan Zhuohua 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2008年第2期280-285,共6页
Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method b... Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-clustering is presented. By integrating the spatial neighbouring information of range data into fuzzy clustering algorithm, a weighted fuzzy clustering algorithm (WFCA) instead of standard clustering algorithm is introduced to realize feature extraction of laser scanner. Aimed at the unknown clustering number in advance, several validation index functions are used to estimate the validity of different clustering algorithms and one validation index is selected as the fitness function of genetic algorithm so as to determine the accurate clustering number automatically. At the same time, an improved genetic algorithm IVGA on the basis of VGA is proposed to solve the local optimum of clustering algorithm, which is implemented by increasing the population diversity and improving the genetic operators of elitist rule to enhance the local search capacity and to quicken the convergence speed. By the comparison with other algorithms, the effectiveness of the algorithm introduced is demonstrated. 展开更多
关键词 laser scanner feature extraction weighted fuzzy clustering validation index genetic algorithm.
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Hierarchical hesitant fuzzy K-means clustering algorithm 被引量:21
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作者 CHEN Na XU Ze-shui XIA Mei-mei 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2014年第1期1-17,共17页
Due to the limitation and hesitation in one's knowledge, the membership degree of an element to a given set usually has a few different values, in which the conventional fuzzy sets are invalid. Hesitant fuzzy sets ar... Due to the limitation and hesitation in one's knowledge, the membership degree of an element to a given set usually has a few different values, in which the conventional fuzzy sets are invalid. Hesitant fuzzy sets are a powerful tool to treat this case. The present paper focuses on investigating the clustering technique for hesitant fuzzy sets based on the K-means clustering algorithm which takes the results of hierarchical clustering as the initial clusters. Finally, two examples demonstrate the validity of our algorithm. 展开更多
关键词 90B50 68T10 62H30 Hesitant fuzzy set hierarchical clustering k-means clustering intuitionisitc fuzzy set
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Genetic Data Clustering Based on Minimum Coding Length
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作者 汪雪红 焦清局 +1 位作者 常盼盼 黄继风 《Agricultural Science & Technology》 CAS 2012年第6期1376-1380,共5页
[Objective] This paper aimed to provide a new method for genetic data clustering by analyzing the clustering effect of genetic data clustering algorithm based on the minimum coding length. [Method] The genetic data cl... [Objective] This paper aimed to provide a new method for genetic data clustering by analyzing the clustering effect of genetic data clustering algorithm based on the minimum coding length. [Method] The genetic data clustering was regarded as high dimensional mixed data clustering. After preprocessing genetic data, the dimensions of the genetic data were reduced by principal component analysis, when genetic data presented Gaussian-like distribution. This distribution of genetic data could be clustered effectively through lossy data compression, which clustered the genes based on a simple clustering algorithm. This algorithm could achieve its best clustering result when the length of the codes of encoding clustered genes reached its minimum value. This algorithm and the traditional clustering algorithms were used to do the genetic data clustering of yeast and Arabidopsis, and the effectiveness of the algorithm was verified through genetic clustering internal evaluation and function evaluation. [Result] The clustering effect of the new algorithm in this study was superior to traditional clustering algorithms, and it also avoided the problems of subjective determination of clustering data and sensitiveness to initial clustering center. [Conclusion] This study provides a new clustering method for the genetic data clustering. 展开更多
关键词 genetic clustering Lossy compression Gaussian distribution Minimum coding length
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K-MEANS CLUSTERING FOR CLASSIFICATION OF THE NORTHWESTERN PACIFIC TROPICAL CYCLONE TRACKS 被引量:4
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作者 余锦华 郑颖青 +2 位作者 吴启树 林金凎 龚振彬 《Journal of Tropical Meteorology》 SCIE 2016年第2期127-135,共9页
Based on the Joint Typhoon Warning Center(JTWC) best-track dataset between 1965 and 2009 and the characteristic parameters including tropical cyclone(TC) position,intensity,path length and direction,a method for objec... Based on the Joint Typhoon Warning Center(JTWC) best-track dataset between 1965 and 2009 and the characteristic parameters including tropical cyclone(TC) position,intensity,path length and direction,a method for objective classification of the Northwestern Pacific tropical cyclone tracks is established by using k-means Clustering.The TC lifespan,energy,active season and landfall probability of seven clusters of tropical cyclone tracks are comparatively analyzed.The characteristics of these parameters are quite different among different tropical cyclone track clusters.From the trend of the past two decades,the frequency of the western recurving cluster(accounting for 21.3% of the total) increased,and the lifespan elongated slightly,which differs from the other clusters.The annual variation of the Power Dissipation Index(PDI) of most clusters mainly depended on the TC intensity and frequency.However,the annual variation of the PDI in the northwestern moving then recurving cluster and the pelagic west-northwest moving cluster mainly depended on the frequency. 展开更多
关键词 tropical cyclone classification of tracks k-means clustering character of cluster
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