基于新直觉模糊相似度的聚类方法

针对现有直觉模糊聚类方法大都未考虑属性(指标)权重,计算过于复杂且计算结果为实数的问题,提出一种基于新直觉模糊相似度的聚类方法,计算结果为直觉模糊数,运用直觉模糊熵得到属性权重,构造了一种考虑属性权重的直觉模糊相似度公式,得到直觉模糊相似矩阵,设计了风险参数,决策者根据自己风险偏好选择风险参数进行聚类.最后通过算例验证了所提出方法的可行性和合理性.     

基于前景和量子理论的模糊多属性决策方法

针对属性权重不完全确定且属性偏好值为区间直觉模糊数的多属性决策问题,提出一种基于前景理论和量子进化算法的模糊多属性决策方法。该方法根据前景理论及模糊数距离公式,定义区间直觉模糊数的前景价值函数,同时将决策者对方案的风险偏好纳入决策行为中,以此来构建方案综合前景值最大化的非线性规划模型。通过引入量子进化算法,求解模型得出最优权重向量。最终根据方案前景值确定出方案的排序。该方法适用于模糊决策环境,能满足决策者不提供确定属性权重的要求,并充分考虑决策者风险心理因素对决策行为的影响,具有广泛的应用价值。数值算例说明了该方法的有效性和可行性。     

基于直觉模糊解析面积的聚类算法

针对现有的直觉模糊集聚类算法对权重的忽视或误用,提出一种基于直觉模糊解析面积的聚类算法.同时给出了直觉模糊集的解析面积和属性权重的计算方法,然后构造了聚类算法的目标函数,并给出聚类算法的详细步骤.算例验证了所提出的算法的合理性与可行性.     

基于策略权重的模糊多属性决策方法

在实际的多属性决策问题中,决策者可以策略性地设置属性权重来获得期望的可选方案排名,这就是策略权重操控问题.决策者的不诚实性在现实中是普遍存在的,对于该问题的研究具有实际价值,而对该问题的研究目前主要针对于确定数.鉴于此,将策略权重操控问题的研究拓展到模糊多属性决策.首先,定义模糊多属性决策的策略权重操控问题和排名范围概念;然后,提出基于可能度和贴近度公式的混合线性规划模型来得出排名范围,并建立决策者策略权重向量最优模型;最后,以决策评价信息为区间数和三角模糊数的多属性决策问题为例,验证所提出模型的可行性和实用性,结果表明,OWA(ordered weighted averaging)算子对策略权重操控抵御能力更强,拓展模型能够保持原有确定数评价信息可选方案排名模型的优越性和准确性.     

区间型数据的模糊c均值聚类算法

提取区间型数据的特征值,给出适用于区间型数据模糊聚类的FCM算法族(IFCM)。该算法适用于不同特征样本数据的模糊聚类运算,并可对聚类结果进行优化。聚类效果的仿真比较表明,IFCM聚类的平均失真度比基于欧氏距离的FCM聚类算法低6.81%。由于距离定义的合理性,IFCM可以根据区间型数据的不同特点调整特征值的聚类权重,并推广至多维类型数据的模糊聚类。     

一种具有区间数信息的多属性大群体决策方法

针对属性值为区间数的多属性多方案大群体决策问题,提出一种区间数聚类算法.通过该聚类算法将方案的属性值聚类,得到方案的群体偏好矩阵,再利用诱导有序加权平均算子集结确定属性的权重,根据方案的综合评价值给出方案排序,进而提出大群体决策方法.该方法较好地避免聚类阈值选取的经验性,提高决策结果的可信度.实例分析验证了方法的有效性和实用性.     

考虑时间序列的动态大群体应急决策方法

针对专家权重和属性权重未知、阶段权重未知且与时间序列有关的动态大群体应急决策问题,提出一种考虑时间序列的动态大群体应急决策方法.首先,提出一个考虑区间直觉模糊数犹豫度的距离公式,定义区间直觉模糊数贴近度,综合考虑贴近度和相似度,用模糊聚类法对大群体专家偏好信息进行聚类;其次,基于现有区间直觉模糊熵公式的不足,提出一个新的区间直觉模糊熵公式,基于此公式考虑专家之间知识水平的差异和各个阶段偏好信息不具遗传性等特点,计算得出专家在不同属性下的权重和属性在各阶段下的权重;再次,考虑时间序列对各阶段权重的影响,构建相对熵模型,对阶段权重进行合理确定,进而利用加权平均算子得到整个决策过程中各方案的综合决策偏好;然后,利用区间直觉模糊数的得分函数和精确函数对方案进行排序,选出最优方案;最后,通过与以往文献的方法对比分析验证所提出方法的有效性和优越性.     

方案偏好已知的三角模糊数型多属性决策方法

研究决策者对方案偏好已知、属性值以三角模糊数形式给出且属性权重信息不能完全确知的多属性决策问题.提出了基于模糊比例值的决策方法和基于模糊偏差度的决策方法,这两种方法首先建立一个线性规划模型,通过求解该模型获得属性权重;然后,基于三角模糊数两两比较的可能度公式及三角模糊数排序公式,对决策方案进行排序和择优;最后,通过实例验证了方法的可行性和有效性.     

一种融合RST与模糊聚类的故障诊断方法

针对模糊聚类方法中存在冗余信息的问题,提出一种融合粗糙集属性约简和模糊等价关系的故障诊断方法,通过应用粗糙集属性约简算法对冗余数据的处理后再应用模糊等价关系聚类获取聚类结果。该方法与单采用模糊等价关系聚类法相比,不仅能够有效减少模糊等价关系矩阵求解中的迭代次数,而且获得聚类数也得到有效降低,并通过实例验证了该方法的有效性和高效性。     

基于聚类有效性分析的模糊粗糙集归纳学习方法

引入模糊C均值聚类算法进行连续属性模糊化,通过聚类有效性分析来确定最佳分类数目,克服了属性模糊化方法需要人为确定划分类数的缺点。用属性模糊化得到的属性隶属度矩阵约简模糊粗糙属性,由此提出一种基于模糊粗糙集的属性约简算法。实例验证了该方法的可行性和有效性。     

An interval weighed fuzzy c-means clustering by genetically guided alternating optimization

The fuzzy c-means (FCM) algorithm is a widely applied clustering technique, but the implicit assumption that each attribute of the object data has equal importance affects the clustering performance. At present, attribute weighted fuzzy clustering has became a very active area of research, and numerous approaches that develop numerical weights have been combined into fuzzy clustering. In this paper, interval number is introduced for attribute weighting in the weighted fuzzy c-means (WFCM) clustering, and it is illustrated that interval weighting can obtain appropriate weights more easily from the viewpoint of geometric probability. Moreover, a genetic heuristic strategy for attribute weight searching is proposed to guide the alternating optimization (AO) of WFCM, and improved attribute weights in interval-constrained ranges and reasonable data partition can be obtained simultaneously. The experimental results demonstrate that the proposed algorithm is superior in clustering performance. It reveals that the interval weighted clustering can act as an optimization operator on the basis of the traditional numerical weighted clustering, and the effects of interval weight perturbation on clustering performance can be decreased.     

Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation

In this paper, a remote sensing image segmentation procedure that utilizes a single point iterative weighted fuzzy C-means clustering algorithm is proposed based upon the prior information. This method can solve the fuzzy C-means algorithm's problem that the clustering quality is greatly affected by the data distributing and the stochastic initializing the centrals of clustering. After the probability statistics of original data, the weights of data attribute are designed to adjust original samples to the uniform distribution, and added in the process of cyclic iteration, which could be suitable for the character of fuzzy C-means algorithm so as to improve the precision. Furthermore, appropriate initial clustering centers adjacent to the actual final clustering centers can be found by the proposed single point adjustment method, which could promote the convergence speed of the overall iterative process and drastically reduce the calculation time. Otherwise, the modified algorithm is updated from multidimensional data analysis to color images clustering. Moreover, with the comparison experiments of the UCI data sets, public Berkeley segmentation dataset and the actual remote sensing data, the real validity of proposed algorithm is proved.     

基于投影技术的三角模糊数型多属性决策方法研究

针对属性权重完全未知且属性值为三角模糊数的多属性决策问题.提出一种基于线性规划和模糊向量投影的决策方法.该方法基于加权属性值离差最大化建立一个线性规划模型,通过求解此模型得到属性的权重,计算各方案的加权属性值在模糊正理想点和负理想点上的投影,进而计算相对贴近度,并据此对方案进行排序,最后,通过算例说明了模型及方法的可行性和有效性.     

基于流数据的模糊聚类算法

对流数据进行有效聚类是一个吸引研究者很大注意力的问题.传统的聚类挖掘算法只能适用于纯数值属性数据或纯分类属性数据,很难适用于混合属性的数据.针对混合属性数据的特点,在借鉴AcluStream算法的基础上,提出了一种模糊聚类算法.算法对流数据的相异度分类度量,定量属性使用欧氏距离和曼哈坦距离度量,定性属性可以采用hamming距离度量.模糊聚类算法的主要步骤有两步:第一步,运用最小距离聚类算法进行聚类,构成一个初始类.第二步,对基于最小距离聚类算法进行聚类所得到的初始簇,运用密度聚类方法进行聚合或分割,使得聚类集合稳定.实践证明:该算法是快速地有效的.     

结合粗糙集和模糊聚类方法的属性约简算法

本文针对粗糙集理论的属性约简算法进行了研究。结合模糊聚类方法，提出了一个新的属性约简算法，用户可以根据实际决策需要和领域知识更改阈值λ，从而得到用户满意的属性约简结果。最后利用该文的算法给出了一个实例的约筒结果。     

基于模糊多属性决策的多维时序信用风险评价建模

针对属性权重完全未知且数据为多维时序的信用风险评价问题,提出基于多属性决策与模糊聚类相结合的混杂信用风险评价建模方法.该方法使用离差最大化方法和二次规划模型得出指标属性的综合权重,对受评样本在各时点上进行多属性决策得到决策评分,再通过决策评分矩阵进行模糊聚类,并对结果进行了有效性验证.最后通过实例验证,证明该方法具有可行性和有效性.     

A Global Clustering Approach Using Hybrid Optimization for Incomplete Data Based on Interval Reconstruction of Missing Value

Incomplete data clustering is often encountered in practice. Here the treatment of missing attribute value and the optimization procedure of clustering are the important factors impacting the clustering performance. In this study, a missing attribute value becomes an information granule and is represented as a certain interval. To avoid intervals determined by different cluster information, we propose a congeneric nearest‐neighbor rule‐based architecture of the preclassification result, which can improve the effectiveness of estimation of missing attribute interval. Furthermore, a global fuzzy clustering approach using particle swarm optimization assisted by the Fuzzy C‐Means is proposed. A novel encoding scheme where particles are composed of the cluster prototypes and the missing attribute values is considered in the optimization procedure. The proposed approach improves the accuracy of clustering results, moreover, the missing attribute imputation can be implemented at the same time. The experimental results of several UCI data sets show the efficiency of the proposed approach.     

一种栅格图层的模糊叠置分析模型

为了更好地进行GIS空间分析，根据GIS应用领域中属性数据的区间值特征，首先利用区间值模糊集来描述模糊属性数据的模糊图层，然后基于区间值模糊集给出了一种栅格图层的模糊叠置分析模型，并改进了基于经典模糊集的模糊叠置分析方法。该模型利用区间值模糊集的基本运算，可以实现普通模糊叠置和加权模糊叠置，而采用区间值，则可以减少属性值模糊性的丢失，且叠置结果符合人们的认知和推理规律，实例结果表明，该模型能够较好地解决区间值属性图层间的模糊叠置分析问题。     

Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining

Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets; for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms (GA); the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms; 2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.’s work in terms of runtime, number of large itemsets and number of association rules.