自动文摘抽取的研究

本文详细介绍了自动文摘抽取的六种方法的基本原理,最后,在潜在语义索引模型选取法的基础上,提出了一种基于LSI语句聚类的语句选取方法     

Chemical characterisation of ancient pottery from south of Italy by Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) Statistical multivariate analysis of data

Chemical characterisation has been carried out on 58 fragments of archaeological pottery from pre-classical sites in Apulia and Lucania, regions in southern Italy. Fourteen elements were determined by atomic emission spectroscopy by using inductively coupled plasma source. Statistical techniques, such as principal component analysis and clustering analysis, have been utilised to define grouping of different pottery items. Tests performed showed that the analysed shards were differentiable mainly by age and provenance.     

Direct Likelihood Evaluation for the Renewal Hawkes Process

An interesting extension of the widely applied Hawkes self-exiting point process, the renewal Hawkes (RHawkes) process, was recently proposed by Wheatley, Filimonov, and Sornette, which has the potential to significantly widen the application domains of the self-exciting point processes. However, they claimed that computation of the likelihood of the RHawkes process requires exponential time and therefore is practically impossible. They proposed two expectation–maximization (EM) type algorithms to compute the maximum likelihood estimator (MLE) of the model parameters. Because of the fundamental role of likelihood in statistical inference, a practically feasible method for likelihood evaluation is highly desirable. In this article, we provide an algorithm that evaluates the likelihood of the RHawkes process in quadratic time, a drastic improvement from the exponential time claimed by Wheatley, Filimonov, and Sornette. We demonstrate the superior performance of the resulting MLEs of the model relative to the EM estimators through simulations. We also present a computationally efficient procedure to calculate the Rosenblatt residuals of the process for goodness-of-fit assessment, and a simple yet efficient procedure for future event prediction. The proposed methodologies were applied on real data from seismology and finance. An R package implementing the proposed methodologies is included in the supplementary materials.     

CAMWI: Detecting protein complexes using weighted clustering coefficient and weighted density

Detection of protein complexes is very important to understand the principles of cellular organization and function. Recently, large protein–protein interactions (PPIs) networks have become available using high-throughput experimental techniques. These networks make it possible to develop computational methods for protein complex detection. Most of the current methods rely on the assumption that protein complex as a module has dense structure. However complexes have core-attachment structure and proteins in a complex core share a high degree of functional similarity, so it expects that a core has high weighted density. In this paper we present a Core-Attachment based method for protein complex detection from Weighted PPI Interactions using clustering coefficient and weighted density. Experimental results show that the proposed method, CAMWI improves the accuracy of protein complex detection.     

Bayesian selection of graphical regulatory models

We define a new class of coloured graphical models, called regulatory graphs. These graphs have their own distinctive formal semantics and can directly represent typical qualitative hypotheses about regulatory processes like those described by various biological mechanisms. They admit an embellishment into classes of probabilistic statistical models and so standard Bayesian methods of model selection can be used to choose promising candidate explanations of regulation. Regulation is modelled by the existence of a deterministic relationship between the longitudinal series of observations labelled by the receiving vertex and the donating one. This class contains longitudinal cluster models as a degenerate graph. Edge colours directly distinguish important features of the mechanism like inhibition and excitation and graphs are often cyclic. With appropriate distributional assumptions, because the regulatory relationships map onto each other through a group structure, it is possible to define a conditional conjugate analysis. This means that even when the model space is huge it is nevertheless feasible, using a Bayesian MAP search, to a discover regulatory network with a high Bayes Factor score. We also show that, like the class of Bayesian Networks, regulatory graphs also admit a formal but distinctive causal algebra. The topology of the graph then represents collections of hypotheses about the predicted effect of controlling the process by tearing out message passers or forcing them to transmit certain signals. We illustrate our methods on a microarray experiment measuring the expression of thousands of genes as a longitudinal series where the scientific interest lies in the circadian regulation of these plants.     

不精确数据的相似度及其在聚类分析中的应用

基本包含度理论,提出不精确数据的一种相似度,并讨论基于此相似度的不精确数据的聚类方法。     

Amalgamation of partitions from multiple segmentation bases: A comparison of non-model-based and model-based methods

The segmentation of customers on multiple bases is a pervasive problem in marketing research. For example, segmentation service providers partition customers using a variety of demographic and psychographic characteristics, as well as an array of consumption attributes such as brand loyalty, switching behavior, and product/service satisfaction. Unfortunately, the partitions obtained from multiple bases are often not in good agreement with one another, making effective segmentation a difficult managerial task. Therefore, the construction of segments using multiple independent bases often results in a need to establish a partition that represents an amalgamation or consensus of the individual partitions. In this paper, we compare three methods for finding a consensus partition. The first two methods are deterministic, do not use a statistical model in the development of the consensus partition, and are representative of methods used in commercial settings, whereas the third method is based on finite mixture modeling. In a large-scale simulation experiment the finite mixture model yielded better average recovery of holdout (validation) partitions than its non-model-based competitors. This result calls for important changes in the current practice of segmentation service providers that group customers for a variety of managerial goals related to the design and marketing of products and services.     

24份普通白菜遗传多样性的形态特征和RAPD分析

利用形态特征与RAPD分析技术,分析了国内不同地区的24份普通白菜(Brassica campestris L.ssp.chinensis(L.)Makino var.communis Tsee et Lee.)种质的遗传多样性.结果表明,13个形态学性状的变异系数为12.4%~69.9%,其中叶柄变异最大,子叶变异最小.24份普通白菜材料在形态学聚类的距离5处聚为4类.利用筛选出的15条RAPD引物从24份普通白菜材料中共扩增出113条带,其中多态性条带83条,多态率73.4%,平均每条引物条带7.53条、多态性条带5.53条.RAPD数据聚类在相似系数0.77处,分为8类,与形态聚类间有重叠和细分.以形态聚类4个类别计算的Nei's基因多样性指标H和Shannon信息指数I,均为第Ⅳ大类第Ⅰ大类第Ⅲ大类第Ⅱ大类,第Ⅳ类的H和I分别为0.2395和0.3523.而不同地区则以江淮地区的Nei's基因多样性H和Shannon信息指数I最高,分别为0.2076和0.3098.这些结果与普通白菜种质资源多样性特征以及地区分布相吻合.     

聚类金字塔树：一种新的高维空间数据索引方法

提出了一种新的有效的高维空间数据索引方法-聚类金字塔树,它先对不均匀分布数据进行聚类处理,然后对聚类的结果实施金字塔分割和存储,由此建立一种有效的索引结构。文中给出聚类金字塔树的几种查询算法。实验证明：处理不均匀分布数据时,聚类金字塔树无论在页面访问次数,还是在CPU总占时间上都优于金字塔树。