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Fuzzy C-Means Algorithm Based on Density Canopy and Manifold Learning
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作者 Jili Chen Hailan Wang Xiaolan Xie 《Computer Systems Science & Engineering》 2024年第3期645-663,共19页
Fuzzy C-Means(FCM)is an effective and widely used clustering algorithm,but there are still some problems.considering the number of clusters must be determined manually,the local optimal solutions is easily influenced ... Fuzzy C-Means(FCM)is an effective and widely used clustering algorithm,but there are still some problems.considering the number of clusters must be determined manually,the local optimal solutions is easily influenced by the random selection of initial cluster centers,and the performance of Euclid distance in complex high-dimensional data is poor.To solve the above problems,the improved FCM clustering algorithm based on density Canopy and Manifold learning(DM-FCM)is proposed.First,a density Canopy algorithm based on improved local density is proposed to automatically deter-mine the number of clusters and initial cluster centers,which improves the self-adaptability and stability of the algorithm.Then,considering that high-dimensional data often present a nonlinear structure,the manifold learning method is applied to construct a manifold spatial structure,which preserves the global geometric properties of complex high-dimensional data and improves the clustering effect of the algorithm on complex high-dimensional datasets.Fowlkes-Mallows Index(FMI),the weighted average of homogeneity and completeness(V-measure),Adjusted Mutual Information(AMI),and Adjusted Rand Index(ARI)are used as performance measures of clustering algorithms.The experimental results show that the manifold learning method is the superior distance measure,and the algorithm improves the clustering accuracy and performs superiorly in the clustering of low-dimensional and complex high-dimensional data. 展开更多
关键词 fuzzy c-means(fcm) cluster center density canopy ISOMAP clustering
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Fuzzy c-means text clustering based on topic concept sub-space 被引量:3
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作者 吉翔华 陈超 +1 位作者 邵正荣 俞能海 《Journal of Southeast University(English Edition)》 EI CAS 2007年第3期439-442,共4页
To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space (TCS2FCM) is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Con... To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space (TCS2FCM) is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Concept phrases, as well as the descriptions of final clusters, are presented using WordNet origin from key phrases. Initial centers and membership matrix are the most important factors affecting clustering performance. Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces. The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision. 展开更多
关键词 TCS2fcm topic concept space fuzzy c-means clustering text clustering
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NEW SHADOWED C-MEANS CLUSTERING WITH FEATURE WEIGHTS 被引量:2
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作者 王丽娜 王建东 姜坚 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2012年第3期273-283,共11页
Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the ... Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms. 展开更多
关键词 fuzzy c-means shadowed sets shadowed c-means feature weights cluster validity index
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A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering 被引量:11
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作者 Yongtao Hu Shuqing Zhang +3 位作者 Anqi Jiang Liguo Zhang Wanlu Jiang Junfeng Li 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2019年第3期156-167,共12页
Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and ... Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method. 展开更多
关键词 Wind TURBINE BEARING FAULTS diagnosis Multi-masking empirical mode decomposition (MMEMD) fuzzy c-mean (fcm) clustering
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Residual-driven Fuzzy C-Means Clustering for Image Segmentation 被引量:9
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作者 Cong Wang Witold Pedrycz +1 位作者 ZhiWu Li MengChu Zhou 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第4期876-889,共14页
In this paper,we elaborate on residual-driven Fuzzy C-Means(FCM)for image segmentation,which is the first approach that realizes accurate residual(noise/outliers)estimation and enables noise-free image to participate ... In this paper,we elaborate on residual-driven Fuzzy C-Means(FCM)for image segmentation,which is the first approach that realizes accurate residual(noise/outliers)estimation and enables noise-free image to participate in clustering.We propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of noise.Built on this framework,a weighted?2-norm regularization term is presented by weighting mixed noise distribution,thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noise.Besides,with the constraint of spatial information,the residual estimation becomes more reliable than that only considering an observed image itself.Supporting experiments on synthetic,medical,and real-world images are conducted.The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers. 展开更多
关键词 fuzzy c-means image segmentation mixed or unknown noise residual-driven weighted regularization
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Improved evidential fuzzy c-means method 被引量:4
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作者 JIANG Wen YANG Tian +2 位作者 SHOU Yehang TANG Yongchuan HU Weiwei 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2018年第1期187-195,共9页
Dempster-Shafer evidence theory(DS theory) is widely used in brain magnetic resonance imaging(MRI) segmentation,due to its efficient combination of the evidence from different sources. In this paper, an improved MRI s... Dempster-Shafer evidence theory(DS theory) is widely used in brain magnetic resonance imaging(MRI) segmentation,due to its efficient combination of the evidence from different sources. In this paper, an improved MRI segmentation method,which is based on fuzzy c-means(FCM) and DS theory, is proposed. Firstly, the average fusion method is used to reduce the uncertainty and the conflict information in the pictures. Then, the neighborhood information and the different influences of spatial location of neighborhood pixels are taken into consideration to handle the spatial information. Finally, the segmentation and the sensor data fusion are achieved by using the DS theory. The simulated images and the MRI images illustrate that our proposed method is more effective in image segmentation. 展开更多
关键词 average fusion spatial information Dempster-Shafer evidence theory(DS theory) fuzzy c-means(fcm) magnetic resonance imaging(MRI) image segmentation
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A KNN-based two-step fuzzy clustering weighted algorithm for WLAN indoor positioning 被引量:3
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作者 Xu Yubin Sun Yongliang Ma Lin 《High Technology Letters》 EI CAS 2011年第3期223-229,共7页
Although k-nearest neighbors (KNN) is a popular fingerprint match algorithm for its simplicity and accuracy, because it is sensitive to the circumstances, a fuzzy c-means (FCM) clustering algorithm is applied to i... Although k-nearest neighbors (KNN) is a popular fingerprint match algorithm for its simplicity and accuracy, because it is sensitive to the circumstances, a fuzzy c-means (FCM) clustering algorithm is applied to improve it. Thus, a KNN-based two-step FCM weighted (KTFW) algorithm for indoor positioning in wireless local area networks (WLAN) is presented in this paper. In KTFW algorithm, k reference points (RPs) chosen by KNN are clustered through FCM based on received signal strength (RSS) and location coordinates. The right clusters are chosen according to rules, so three sets of RPs are formed including the set of k RPs chosen by KNN and are given different weights. RPs supposed to have better contribution to positioning accuracy are given larger weights to improve the positioning accuracy. Simulation results indicate that KTFW generally outperforms KNN and its complexity is greatly reduced through providing initial clustering centers for FCM. 展开更多
关键词 wireless local area networks (WLAN) indoor positioning k-nearest neighbors (KNN) fuzzy c-means (fcm clustering center
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Fuzzy C-Means Clustering Based Phonetic Tied-Mixture HMM in Speech Recognition 被引量:1
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作者 徐向华 朱杰 郭强 《Journal of Shanghai Jiaotong university(Science)》 EI 2005年第1期16-20,共5页
A fuzzy clustering analysis based phonetic tied-mixture HMM(FPTM) was presented to decrease parameter size and improve robustness of parameter training. FPTM was synthesized from state-tied HMMs by a modified fuzzy C-... A fuzzy clustering analysis based phonetic tied-mixture HMM(FPTM) was presented to decrease parameter size and improve robustness of parameter training. FPTM was synthesized from state-tied HMMs by a modified fuzzy C-means clustering algorithm. Each Gaussian codebook of FPTM was built from Gaussian components within the same root node in phonetic decision tree. The experimental results on large vocabulary Mandarin speech recognition show that compared with conventional phonetic tied-mixture HMM and state-tied HMM with approximately the same number of Gaussian mixtures, FPTM achieves word error rate reductions by 4.84% and 13.02% respectively. Combining the two schemes of mixing weights pruning and Gaussian centers fuzzy merging, a significantly parameter size reduction was achieved with little impact on recognition accuracy. 展开更多
关键词 speech recognition hidden Markov model (HMM) fuzzy c-means (fcm) phonetic decision tree
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Fault Pattern Recognition based on Kernel Method and Fuzzy C-means
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作者 SUN Yebei ZHAO Rongzhen TANG Xiaobin 《International Journal of Plant Engineering and Management》 2016年第4期231-240,共10页
A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the c... A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means (FCM) is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery. 展开更多
关键词 Kernel method fuzzy c-means fcm pattern recognition CLUSTERING
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A weighted fuzzy C-means clustering method for hardness prediction
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作者 Yuan Liu Shi-zhong Wei 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2023年第1期176-191,共16页
The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for d... The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for data clustering using improved Mahalanobis distance based on random forest importance values,which could play a full role of important features and avoid clustering center overlap.The samples were divided into two classes.The top 10 features of each class were selected to form two feature subsets for better performance of the model.The dimension and dispersion of features decreased in such feature subsets.Comparing four machine learning algorithms,SVR had the best performance and was chosen to modeling.The hyper-parameters of the SVR model were optimized by particle swarm optimization.The samples in validation set were classified according to minimum distance of sample to clustering centers,and then the SVR model trained by feature subset of corresponding class was used for prediction.Compared with the feature subset of original data set,the predicted values of model trained by feature subsets of classified samples by WFCM had higher correlation coefficient and lower root mean square error.It indicated that WFCM was an effective method to reduce the dispersion of features and improve the accuracy of model. 展开更多
关键词 Hardness prediction weighted fuzzy c-means algorithm Feature selection Particle swarm optimization Support vector regression Dispersion reduction
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Intelligent diagnosis of the solder bumps defects using fuzzy C-means algorithm with the weighted coefficients 被引量:2
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作者 LU XiangNing SHI TieLin +3 位作者 WANG SuYa LI Li Yi SU Lei LIAO GuangLan 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2015年第10期1689-1695,共7页
Solder bump technology has been widely used in electronic packaging. With the development of solder bumps towards higher density and finer pitch, it is more difficult to inspect the defects of solder bumps as they are... Solder bump technology has been widely used in electronic packaging. With the development of solder bumps towards higher density and finer pitch, it is more difficult to inspect the defects of solder bumps as they are hidden in the package. A nondestructive method using the transient active thermography has been proposed to inspect the defects of a solder bump, and we aim at developing an intelligent diagnosis system to eliminate the influence of emissivity unevenness and non-uniform heating on defects recognition in active infrared testing. An improved fuzzy c-means(FCM) algorithm based on the entropy weights is investigated in this paper. The captured thermograms are preprocessed to enhance the thermal contrast between the defective and good bumps. Hot spots corresponding to 16 solder bumps are segmented from the thermal images. The statistical features are calculated and selected appropriately to characterize the status of solder bumps in FCM clustering. The missing bump is identified in the FCM result, which is also validated by the principle component analysis. The intelligent diagnosis system using FCM algorithm with the entropy weights is effective for defects recognition in electronic packages. 展开更多
关键词 solder bump fuzzy c-means clustering feature weighting principal component analysis
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基于FCM的快速模糊聚类算法研究 被引量:9
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作者 匡平 朱清新 陈旭东 《电子测量与仪器学报》 CSCD 2007年第2期15-20,共6页
为改善FCM算法的运算性能、获得和原FCM算法等价的分类结果,本文提出了基于加权样本的fFCM(fast FCM)算法。此算法首先构造原待聚类集合的权集,并在权集上应用改进的FCM算法——WFCM(weighted FCM)算法快速获得和原FCM算法近似的分割结... 为改善FCM算法的运算性能、获得和原FCM算法等价的分类结果,本文提出了基于加权样本的fFCM(fast FCM)算法。此算法首先构造原待聚类集合的权集,并在权集上应用改进的FCM算法——WFCM(weighted FCM)算法快速获得和原FCM算法近似的分割结果;然后,将得到的分割结果作为FCM算法的初值再次利用FCM算法以获得最终的分割结果。理论证明和相关实验表明,fFCM不仅能获得和原FCM算法等价的分类结果,还具有良好的运算性能,具有广泛的适用性。 展开更多
关键词 模糊C均值聚类 weighted fuzzy c-means(Wfcm) 加权样本 图像分割
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基于加权样本的FCM快速算法研究 被引量:3
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作者 匡平 朱清新 +2 位作者 陈叙东 王明文 卿利 《四川大学学报(工程科学版)》 EI CAS CSCD 北大核心 2005年第6期130-134,共5页
为改进FCM算法在处理大样本集聚类时速度慢、耗时多的缺点,根据样本在特征空间中的特征值分布情况,引入等价样本和样本加权概念,在此基础上提出了FCM(Fuzzy C-Means)的快速算法一般形式:WFCM(WeightedFuzzy C-Means)算法。理论上证明了W... 为改进FCM算法在处理大样本集聚类时速度慢、耗时多的缺点,根据样本在特征空间中的特征值分布情况,引入等价样本和样本加权概念,在此基础上提出了FCM(Fuzzy C-Means)的快速算法一般形式:WFCM(WeightedFuzzy C-Means)算法。理论上证明了WFCM算法和FCM算法对样本集分割的等价性,并且,WFCM在运算性能方面明显优于FCM算法。而两个算法在灰度图像分割上的例子验证了WFCM算法的快速性和有效性。 展开更多
关键词 模糊C均值聚类 fcm算法 Wfcm算法 加权样本 图像分割
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一种有效的FCM算法的实现方式 被引量:9
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作者 石洪波 于剑 黄厚宽 《铁道学报》 EI CAS CSCD 北大核心 2003年第1期63-67,共5页
提出了一种有效的FCM算法的实现方式。目前FCM算法的实现大多采用启发式方法,根据经验或实验用人工选择的方法来确定FCM算法中的所有参数。利用一个有关选择权指数m的新的研究结果,提出了一种有效的FCM算法的实现方式,选择了一种简便的... 提出了一种有效的FCM算法的实现方式。目前FCM算法的实现大多采用启发式方法,根据经验或实验用人工选择的方法来确定FCM算法中的所有参数。利用一个有关选择权指数m的新的研究结果,提出了一种有效的FCM算法的实现方式,选择了一种简便的聚类有效性函数用于聚类结果的检验,最后用数值实验验证了方法的合理性。 展开更多
关键词 模糊C-均值(fcm)算法 权指数 聚类有效性
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加权空间函数优化FCM的SAR图像分割 被引量:14
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作者 田小林 焦李成 缑水平 《西安电子科技大学学报》 EI CAS CSCD 北大核心 2008年第5期846-852,共7页
传统模糊c-均值聚类算法没有考虑图像像素空间信息特征,在应用于合成孔径雷达图像分割时,由于合成孔径雷达图像中斑点噪声的影响,通常不能得到正确的分割结果.基于此问题提出加权空间隶属度和加权空间函数并应用于c-均值聚类算法,加权... 传统模糊c-均值聚类算法没有考虑图像像素空间信息特征,在应用于合成孔径雷达图像分割时,由于合成孔径雷达图像中斑点噪声的影响,通常不能得到正确的分割结果.基于此问题提出加权空间隶属度和加权空间函数并应用于c-均值聚类算法,加权空间隶属度是多尺度条件下空间各相邻像素的位置和强度信息的加权隶属度值,加权空间函数中各加权空间隶属度的影响系数由自适应遗传算法优化,最终的隶属度值由加权空间函数修正.由于在这种聚类过程中融入了优化的空间信息,因此弱化了斑点噪声的影响,提高了分割精度.这种算法应用于实际合成孔径雷达图像分割实验,结果表明此算法对初始分类结果不敏感,具有较强的抗噪性能,改善了SAR图像的分割结果. 展开更多
关键词 合成孔径雷达图像分割 C-均值聚类算法 加权空间函数 自适应遗传算法
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FCM聚类算法中模糊加权指数m的优化 被引量:6
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作者 石海霞 叶水生 《计算机应用与软件》 CSCD 2009年第5期62-64,73,共4页
研究模糊加权指数m对FCM(Fuzzy c-means)算法的聚类性能的影响,从划分熵入手提出了变权划分熵的概念,并基于模糊决策理论提出了一种最优加权指数m*的选取方法。该方法利用小的目标函数值和小的变权划分熵对应好的数据分类结果这一特性,... 研究模糊加权指数m对FCM(Fuzzy c-means)算法的聚类性能的影响,从划分熵入手提出了变权划分熵的概念,并基于模糊决策理论提出了一种最优加权指数m*的选取方法。该方法利用小的目标函数值和小的变权划分熵对应好的数据分类结果这一特性,将m的确定转化为一个带约束的非线性规划问题,从而确定最佳取值m*。实验结果表明该方法是非常有效和灵敏的。 展开更多
关键词 模糊加权指数 fcm 变权划分熵 模糊决策
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基于FCM与集成高斯过程回归的赖氨酸发酵软测量 被引量:9
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作者 嵇小辅 张翔 《智能系统学报》 CSCD 北大核心 2015年第1期156-162,共7页
为解决赖氨酸发酵过程中菌体浓度难以在线检测的难题,提出一种基于模糊C均值聚类(FCM)与集成高斯过程回归(GPR)的软测量建模方法。针对典型生物发酵过程可分为延滞期、指数生长期、稳定期、死亡期4个反应周期的特点,采用模糊C均值聚类... 为解决赖氨酸发酵过程中菌体浓度难以在线检测的难题,提出一种基于模糊C均值聚类(FCM)与集成高斯过程回归(GPR)的软测量建模方法。针对典型生物发酵过程可分为延滞期、指数生长期、稳定期、死亡期4个反应周期的特点,采用模糊C均值聚类算法对样本集进行聚类分析以形成若干子样本集;对每个子样本集分别采用高斯过程回归训练时,为提高GPR模型的泛化能力,利用Adaboost算法提升GPR模型,分别在各子集建立集成GPR软测量子模型;采用欧氏距离计算新样本点对应于每一子模型的隶属度;加权求和获得最终的软测量模型的预测输出。基于氨基酸类典型菌种L-赖氨酸反应过程菌体浓度参数预测的试验研究表明:与全局单一GPR模型、集成GPR模型和基于FCM与多GPR模型相比,所建立的基于FCM与集成GPR软测量模型拟合精度高,泛化能力强,较好地满足了赖氨酸发酵过程的控制要求。 展开更多
关键词 高斯过程回归(GPR) 模糊C均值聚类(fcm) ADABOOST算法 L-赖氨酸 软测量 欧氏距离 隶属度 加权求和
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基于FCM-组合赋权的难采储量分类 被引量:1
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作者 杨娟 龚承柱 诸克军 《运筹与管理》 CSSCI CSCD 北大核心 2014年第2期237-243,共7页
目前储量的分类标准是通过划分指标值的范围来确定的,这就要求所有指标值恰好符合既定的指标范围,否则难以划分储量类别。为克服这一问题,本文结合模糊c-均值算法和组合赋权法实现难采储量的分类。首先基于效益指标运用模糊c-均值算法... 目前储量的分类标准是通过划分指标值的范围来确定的,这就要求所有指标值恰好符合既定的指标范围,否则难以划分储量类别。为克服这一问题,本文结合模糊c-均值算法和组合赋权法实现难采储量的分类。首先基于效益指标运用模糊c-均值算法自动搜索储量的最佳类别,再利用主客观赋权偏差最小的思想,构建组合赋权模型,确定属性指标的权重,并计算储量效益指标值,结合模糊c-均值结果判别难采储量类别。最后以大庆某油田为实例,对其难采储量进行了分类,有效指导难采储量滚动开发决策。 展开更多
关键词 储量分类 模糊C-均值 组合赋权 难采储量
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基于自适应模糊加权指数的FCM聚类测量图像分割方法 被引量:5
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作者 李晓冰 《红外技术》 CSCD 北大核心 2013年第3期146-149,共4页
针对采用FCM聚类进行测量图像分割时,模糊加权指数难以确定的问题,通过分析FCM聚类原理,依据测量图像分割的具体要求,根据加权指数对不同模糊聚类过程的作用程度,提出了一种基于自适应模糊加权指数的FCM聚类测量图像分割方法。实验结果... 针对采用FCM聚类进行测量图像分割时,模糊加权指数难以确定的问题,通过分析FCM聚类原理,依据测量图像分割的具体要求,根据加权指数对不同模糊聚类过程的作用程度,提出了一种基于自适应模糊加权指数的FCM聚类测量图像分割方法。实验结果表明:该算法可以减少聚类迭代次数,确保分类的准确性,提高图像分割质量。 展开更多
关键词 图像分割 fcm聚类 模糊加权指数 收敛速度 测量图像
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Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination
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作者 Xianghui Hu Yiming Tang +2 位作者 Witold Pedrycz Jiuchuan Jiang Yichuan Jiang 《Computers, Materials & Continua》 SCIE EI 2024年第9期4917-4945,共29页
Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric data.Recently,knowledge hints have ... Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric data.Recently,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge hints.However,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation indices.Moreover,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to knowledgemisguidance.To solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density points.First,a newdatadensitycalculation function is proposed.The Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge hints.Then,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data structure.Finally,the initial number of clusters is set to be greater than the true one based on the number of knowledge hints.Then,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination mechanism.Through experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed. 展开更多
关键词 fuzzy c-means(fcm) possibilistic clustering optimal number of clusters knowledge-driven machine learning fuzzy logic
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