基于粗糙集和混合聚类法的决策表约简算法

在分析粗糙集理论、分层聚类算法和k-means聚类算法的基础上,提出一种基于粗糙集和混合聚类法的决策表约简算法,该算法首先是使用基于分层聚类的k-means混合聚类法离散化决策表中的连续属性,然后利用粗糙集理论对离散后的决策表进行属性约简,得到决策规则集,并通过在铁路客运量预测系统中的应用验证了算法的可行性和有效性.

模糊混合聚类法在福州地热水有害成分超标程度评价中的应用

来自深部循环的地热水化学成分比较复杂 ,为能安全而有效地使用地下热水 ,须对地热水中有害成分的含量进行分析评价。考虑到评价分级过程中存在的模糊性 ,文章从模糊集的理论与方法出发 ,将模糊聚类最大矩阵元原理与基于数据迭代为基础的模糊ISODATA聚类分析相结合 ,形成模糊混合聚类法。利用该方法对福建省福州市地热水有害成分超标程度进行了分类评价。在地热田区沿断层走向选取典型钻孔 ,对其中的地下热水水质有害成分超标程度进行最优分类 ,划分等级 ;并通过对地热水有害成分因子的分类 ,分析出引起有害成分超标的最主要的有害成分因子为F- 、pH、H2

辽西沿海河流分类的模糊混合聚类法

将模糊聚类最大矩阵元原理与最小二乘最优准则下的模糊ISODATA聚类迭代原理相结合,并按最大隶属原则确定最优分类,建立了基于迭代的模糊混合聚类法.利用此方法对辽西沿海诸河流域进行了分类评价,得到了令人满意的结果。

模糊混合聚类法在农药污染研究的应用

本文综述性地介绍了模糊混合聚类法的基本原理,并用商丘大吴庄均衡试验区的土壤乐果含量的实测资料,具体说明了模糊混合聚类法在农药污染研究中的应用过程。

粗糙集、神经网络和专家系统模型用于电力系统故障诊断

针对电力系统变电所故障诊断系统中含有大量不确定信息和实时性要求高的特点,以电力系统变电所开关保护信息为基础,基于智能互不融合的思想,将粗糙集、神经网络和专家系统有机结合在一起,提出一种电力系统变电所故障诊断的新方法。首先在数据采集和预处理的基础上,利用混合聚类法对原始故障诊断样本进行离散化处理,然后利用粗糙集理论对样本决策表进行属性约简,删除冗余信息,得到能够覆盖原始数据特征的具有最小条件属性的相应学习样本集。再运用径向基函数(RBF)神经网络对故障诊断知识进行模式识别,并结合专家系统,利用其推理判断能力,对RBF神经网络的某些输出结果进行必要的修正。最后通过故障诊断实例,说明了方法的有效性。

Brain MRI Segmentation Using KFCM and Chan-Vese Model

To extract region of interests （ROI） in brain magnetic resonance imaging （MRI） with more than two objects and improve the segmentation accuracy, a hybrid model of a kemel-based fuzzy c-means （KFCM） clustering algorithm and Chan-Vese （CV） model for brain MRI segmentation is proposed. The approach consists of two succes- sive stages. Firstly, the KFCM is used to make a coarse segmentation, which achieves the automatic selection of initial contour. Then an improved CV model is utilized to subdivide the image. Fuzzy membership degree from KFCM clus- tering is incorporated into the fidelity term of the 2-phase piecewise constant CV model to obtain accurate multi-object segmentation. Experimental results show that the proposed model has advantages both in accuracy and in robustness to noise in comparison with fuzzy c-means （FCM） clustering, KFCM, and the hybrid model of FCM and CV on brain MRI segmentation.

SCMR:a semantic-based coherence micro-cluster recognition algorithm for hybrid web data stream

Data aggregation from various web sources is very significant for web data analysis domain. In ad- dition, the recognition of coherence micro cluster is one of the most interesting issues in the field of data aggregation. Until now, many algorithms have been proposed to work on this issue. However, the deficiency of these solutions is that they cannot recognize the micro-cluster data stream accurately. A semantic-based coherent micro-cluster recognition algorithm for hybrid web data stream is nronosed.Firstly, an objective function is proposed to recognize the coherence micro-cluster and then the coher- ence micro-cluster recognition algorithm for hybrid web data stream based on semantic is raised. Fi-

Refracturing candidate selection for MFHWs in tight oil and gas reservoirs using hybrid method with data analysis techniques and fuzzy clustering

The selection of refracturing candidate is one of the most important jobs faced by oilfield engineers. However, due to the complicated multi-parameter relationships and their comprehensive influence, the selection of refracturing candidate is often very difficult. In this paper, a novel approach combining data analysis techniques and fuzzy clustering was proposed to select refracturing candidate. First, the analysis techniques were used to quantitatively calculate the weight coefficient and determine the key factors. Then, the idealized refracturing well was established by considering the main factors. Fuzzy clustering was applied to evaluate refracturing potential. Finally, reservoirs numerical simulation was used to further evaluate reservoirs energy and material basis of the optimum refracturing candidates. The hybrid method has been successfully applied to a tight oil reservoir in China. The average steady production was 15.8 t/d after refracturing treatment, increasing significantly compared with previous status. The research results can guide the development of tight oil and gas reservoirs effectively.

多站空情航迹优化及分类指挥决策方式研究

空情是防空指挥的依据,本文提出了集团军防空兵利用本级多站空情,优化二次空情航迹的Fourier分析法,通过处理S项后的高频项,分离干扰等“噪声”过程,为反空袭指挥决策提供合理空情。当大批空中目标来袭时,依据处理的多批多站空情,集团军防空兵如何实现新的分类指挥,在文[1]基础上,本文引入等混合聚类法这一新的批次分类模式,对批次多因素分类,此法控制参量多、收敛速度快,使分类指挥决策的时效性得到提高,并结合示例计算与对比。

The Methodology of Application Development for Hybrid Architectures

This paper provides an overview of the main recommendations and approaches of the methodology on parallel computation application development for hybrid structures. This methodology was developed within the master＇s thesis project ＂Optimization of complex tasks＇ computation on hybrid distributed computational structures＂ accomplished by Orekhov during which the main research objective was the determination of＂ patterns of the behavior of scaling efficiency and other parameters which define performance of different algorithms＇ implementations executed on hybrid distributed computational structures. Major outcomes and dependencies obtained within the master＇s thesis project were formed into a methodology which covers the problems of applications based on parallel computations and describes the process of its development in details, offering easy ways of avoiding potentially crucial problems. The paper is backed by the real-life examples such as clustering algorithms instead of artificial benchmarks.

An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering

The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the Kmeans algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley's Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.

Self-organizing dual clustering considering spatial analysis and hybrid distance measures

Dual clustering performs object clustering in both spatial and non-spatial domains that cannot be dealt with well by traditional clustering methods.However,recent dual clustering research has often omitted spatial outliers,subjectively determined the weights of hybrid distance measures,and produced diverse clustering results.In this study,we first redefined the dual clustering problem and related concepts to highlight the clustering criteria.We then presented a self-organizing dual clustering algorithm (SDC) based on the self-organizing feature map and certain spatial analysis operations,including the Voronoi diagram and polygon aggregation and amalgamation.The algorithm employs a hybrid distance measure that combines geometric distance and non-spatial similarity,while the clustering spectrum analysis helps to determine the weight of non-spatial similarity in the measure.A case study was conducted on a spatial database of urban land price samples in Wuhan,China.SDC detected spatial outliers and clustered the points into spatially connective and attributively homogenous sub-groups.In particular,SDC revealed zonal areas that describe the actual distribution of land prices but were not demonstrated by other methods.SDC reduced the subjectivity in dual clustering.