基于模糊聚类的神经网络模型及其在渗流分析中的应用

本文采用模糊聚类理论方法对因子集进行模糊聚类 ,然后利用神经网络的方法建立样本因子集类别变量特征值与样本观测值之间的预测模型 ,提出了将模糊聚类、模糊模式识别以及神经网络三者有机结合的预测理论。并通过某大坝渗流计算实例对传统的统计预报模型和基于模糊聚类的神经网络预测模型进行了比较 ,结果表明后者的预报精度比前者要高。     

An efficient prototype merging strategy for the condensed 1-NN rule through class-conditional hierarchical clustering

A generalized prototype-based classification scheme founded on hierarchical clustering is proposed. The basic idea is to obtain a condensed 1-NN classification rule by merging the two same-class nearest clusters, provided that the set of cluster representatives correctly classifies all the original points. Apart from the quality of the obtained sets and its flexibility which comes from the fact that different intercluster measures and criteria can be used, the proposed scheme includes a very efficient four-stage procedure which conveniently exploits geometric cluster properties to decide about each possible merge. Empirical results demonstrate the merits of the proposed algorithm taking into account the size of the condensed sets of prototypes, the accuracy of the corresponding condensed 1-NN classification rule and the computing time.     

基于镜头的视频场景构造方法研究

由于内容颗粒度地小,镜头层次的检索不能满足视频内容使用的需要。场景比镜头高一个层次的视频内容结构单,能在一定程度上缓解镜头颗粒度过小的问题。“场景”是一组镜头的集合,在内容上包含相似的对象或包含类似的背景。本文提出了一种基于镜头构造频场景的思路,包括三个环节：镜头边界探测,镜头特征提取和镜头聚类。     

模糊聚类在人才选拔中的应用

从电力与环境的关系入手，分析电力生产的环境影响问题及其改善途径。简介日趋势严格的国际性环保条约、规定和有关排放标准，以及由引而导电力工业生产技术改进与发展趋势。最后展望了我国未来电力与环境协调发展的规划和前景。     

A New Method for Extraction of Fuzzy Rules from Small Samples

0~adosAdesignmethodisselectedaccoldingtotheobjectandtheenvironmentfortheconealengineeringandsimulahonsystem.ThemathematicalmodelingisalmoStimPOssibleforsomecomplexcontrolsystem.fothefuZZycontroliswidelyappliedforitSindependenceofthemathematicalmodelingOftheobject["'].However,expertcontrolrulesaregenelallyreqUiredforsystemdesign.Whatcanbeobtainedinsomecasesarenumericalsampledatafromcontroloperationsensorsratherthansuchcontrolrules.ThisreqUirestheextractionof"if-then"fUZZyconealrulesfromthe…     

The optimal graph partitioning problem

Summary In this paper we consider the problem of partitioning the set of nodes in a graph in at mostp classes, such that the sum of node weights in any class is not greater than the class capacityb, and such that the sum of edge weights, for edges connecting nodes in the same class, is maximal. This problem can be formulated as a MILP, which turns out to be completely symmetrical with respect to thep classes, and the gap between the relaxed LP solution and the optimal solution is the largest one possible. These two properties make it very difficult to solve even smaller problems. In this paper it is shown how it is possible to preassign certain nodes to certain classes, thus reducing both the symmetric nature of the formulation, the number of variables and constraints and the gap. It is also shown how the gap can be reduced even further by introducing combinatorial cuts. Computational results based on the two formulations of the problem and combinatorial cuts are presented.     

Neural navigation interfaces for Information Retrieval: Are they more than an appealing idea?

Neural networks have recently been proposed for the construction of navigation interfaces for Information Retrieval systems. In this paper, we give an overview of some current research in this area. Most of the cited approaches use (variants) of the well-known Kohonen network. The Kohonen network implements a topology-preserving dimensionality-reducing mapping, which can be applied for information visualization. We identify a number of problems in the application of Kohonen networks for Information Retrieval, most notably scalability, reliability and retrieval effectiveness. To solve these problems we propose to use the Growing Cell Structures network, a variant of the Kohonen network which shows a more flexible adaptation to the domain structure.This network was tested on two standard test-collections, using a combined recall and precision measure, and compared to traditional IR methods such as the Vector Space Model and various clustering algorithms. The network performs at a competitive level of effectiveness, and is suitable for visualization purposes. However, the incremental training procedures for the networks result in a reliability problem, and the approach is computationally intensive. Also, the utility of the resulting maps for navigation will need further improvement.     

Bax-induced cell death in Candida albicans

Bax is a pro-apoptotic member of the Bcl-2 family of proteins involved in the regulation of genetically programmed cell death in mammalian cells. It has been shown that heterologous expression of Bax in several yeast species, such as Saccharomyces cerevisiae, Schizosaccharomyces pombe and Pichia pastoris, also induces cell death. In this study we investigated the effects of Bax expression in the pathogenic yeast Candida albicans. Cell death inducing expression of Bax required a synthetic BAX gene that was codon-optimized for expression in Candida albicans. Expression of this BAX gene resulted in growth inhibition and cell death. By fusing Bax with the yeast enhanced green fluorescent protein of Aequoria victoria, the cell death-inducing effect of Bax was increased due to reduced proteolytic degradation of Bax. Using this fusion protein we showed that, upon expression in C. albicans, Bax co-localizes with the mitochondria. Furthermore, we showed for the first time that expression of Bax in yeast causes the mitochondria, which are normally distributed throughout the cell, to cluster in the perinuclear region.     

Query-Sensitive Similarity Measures for Information Retrieval

The application of document clustering to information retrieval has been motivated by the potential effectiveness gains postulated by the cluster hypothesis. The hypothesis states that relevant documents tend to be highly similar to each other and therefore tend to appear in the same clusters. In this paper we propose an axiomatic view of the hypothesis by suggesting that documents relevant to the same query (co-relevant documents) display an inherent similarity to each other that is dictated by the query itself. Because of this inherent similarity, the cluster hypothesis should be valid for any document collection. Our research describes an attempt to devise means by which this similarity can be detected. We propose the use of query-sensitive similarity measures that bias interdocument relationships toward pairs of documents that jointly possess attributes expressed in a query. We experimentally tested three query-sensitive measures against conventional ones that do not take the query into account, and we also examined the comparative effectiveness of the three query-sensitive measures. We calculated interdocument relationships for varying numbers of top-ranked documents for six document collections. Our results show a consistent and significant increase in the number of relevant documents that become nearest neighbors of any given relevant document when query-sensitive measures are used. These results suggest that the effectiveness of a cluster-based information retrieval system has the potential to increase through the use of query-sensitive similarity measures.     

Note on the relationship between probabilistic and fuzzy clustering

In this short communication, based on Renyi entropy measure, a new Renyi information based clustering algorithm A is presented. Algorithm A and the well-known fuzzy clustering algorithm FCM have the same clustering track. This fact builds the very bridge between probabilistic clustering and fuzzy clustering, and fruitful research results on Renyi entropy measure may help us to further understand the essence of fuzzy clustering.This work was supported in part by the RGC CERG grant under project HongKong PolyU 5065/98E