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Thereare two contributions in this paper. First, by regarding every base clustering as one attribute of the original data, it has found that the algorithm based on that is more extendable and flexible. Second, it designs a latent variable cluster ensemble model in this way and infers the algorithm of the model with Markov chain Monte Carlo approximation.

主要工作包括:第一,分析了把每一个基聚类器看成是原数据的一个属性这种处理方式的优越性,发现按此方法建立起来的聚类集成算法就具有良好的扩展性和灵活性;第二,在此基础之上,建立了latent variable cluster ensemble概率模型进行聚类集成,并且给出了LVCE 模型的Markovchain Monte Carlo算法。

The selection of starting center points of clustering has great effects on the constringency speed of this clustering algorithms and the performance of clustering.

聚类初始中心的选择对该聚类算法的收敛速度和聚类的性能都有很大的影响。

The effects on core collection development with different sample proportionsDuring core collection development a difficult task comes when attempting to reduce the sample size of a large collection without losing its diversity.

不同聚类方法和取样策略的评价聚类方法与核心库的构建有着密切关系,不同的聚类方法对稻米品质性状核心种质的构建可以产生不同影响。

The patterns are unlabeled and, therefore, there is no explicit correspondence between cluster labels in different partitions of an ensemble. An extra complexity arises when different partitions contain different numbers of clusters, often resulting in a label correspondence problem.

一方面,聚类的模式是未标记的,由不同聚类算法得到的标记之间无明显的联系;另一方面,各种划分可能含有不同的聚类个数,这就涉及到标记对应问题。

DECA searches for the optimal cluster number by using the improved genetic techniques to optimize string lengths of chromosomes at the same time, the convergence of clustering center parameters is expedited with the help of Fuzzy C-Means algorithm.

针对模糊聚类算法不适应复杂环境的问题,提出了一种新的动态进化聚类算法,克服了传统模糊聚类建模算法须事先确定规则数的缺陷。

DECA searches for the optimal cluster number by using the improved genetic techniques to optimize string lengths of chromosomes;at the same time,the convergence of clustering center parameters is expedited with the help of Fuzzy C-Means algorithm.

彭玲针对模糊聚类算法不适应复杂环境的问题,提出了一种新的动态进化聚类算法,克服了传统模糊聚类建模算法须事先确定规则数的缺陷。

Experimental results based on mean square error and user evaluation showed that the proposed method outperforms traditional ones.

基于均方差和用户评估的实验结果表明,新的聚类方法较传统的聚类方法具有更好的聚类效果。

The similarity measure of the clustering objects heavily affects the cluttering result, so the author studies the problems about similarity measure, and proposes a generalized distance.

聚类对象的相似性度量对聚类结果的影响很大,因而研究了聚类中的相似性度量问题,提出了广义距离概念。

However, most of these methods are not effective on handling practical datasets for the reason of incapability of tackling the various densities.

然而现有的基于密度的聚类算法由于缺乏处理不同密度聚类的能力,在聚类实际数据集时效果往往不能令人满意。

In order to overcome the technological difficulties encountered in the course of insulation fault diagnosis on the base of dissolved gases analysis, several kinds of mathematic models and actualized methods are brought forward to improve the reliability and veracity of fault diagnosis of transformers. The research works are shown mainly as followings:1 A modified fuzzy multi-criteria method is brought forward for insulation fault diagnosis of transformer. Furthermore, a new method for insulation fault diagnosis is proposed on the base of fuzzy multi-criteria together with rule reasoning. With fuzzy diagnosis in the method, fault reasons are filtrated and then the reasons in low probability are prohibited, Moreover, the left reasons are testified by using rule reasoning and then final concludes are drawn in much less misjudge probability and better results.2 On the base of geometry characteristics of C- partition to sample set of DGA data of transformer, a method is put forward to compute effective radius of neighbor field of a sample, number of clusters and values of initial centers. Moreover, the fuzzy C- means cluster model with adaptive weight is brought forward in the first time and then the fault classifier is designed for insulation fault diagnosis of transformer.3 According to the weakness of the degree of gray of gray incidence, a new formula to compute DGI is put forward and then incidence order criterion is ascertained. Moreover, a new DGI model for fault diagnosis is proposed according to the further analysis to relationship between fault reason and content of oil dissolved gases of transformer.

为解决在应用油中溶解气体分析方法(Dissolved Gases Analysis,简称DGA)诊断变压器内部绝缘故障时所遇到的主要技术难题,论文提出了用于提高变压器故障诊断准确性和可靠性的数学模型及实现方法,主要研究工作如下:1)提出了一种改进的变压器绝缘故障诊断模糊综合评判方法;进一步提出了将模糊综合诊断与规则推理相结合进行绝缘故障诊断的方法,该方法采用模糊推理对故障原因进行"过滤",滤掉可能性极小的原因,然后进一步利用规则推理验证剩下的可能原因,得出最终结论,大大减少了误判比率,获得了较好的效果。2)根据以变压器DGA数据为特征量的样本空间的c-划分几何特性出发,提出了一种求取样本有效邻域半径和聚类数及聚类中心初值的方法,在此基础上,首次提出了一种自适应加权的变压器绝缘故障诊断的模糊c-均值聚类模型,并设计出故障分类器。3)针对常用灰色关联度的不足,提出了一新的灰色关联度计算公式及确定关联序的准则;并在此基础上,通过深入分析变压器发生绝缘故障时的原因与油中溶解特征气体含量的关系,建立了一种新的故障诊断灰色关联模型;通过实例分析证明,该方法能有效地诊断出变压器绝缘故障及故障部位,大大提高了诊断的准确性。

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