电子政务系统中面向公众的个性化信息服务模型

电子政务作为Internet的主要应用领域，已受到世界各国的普遍关注。面对公众各种服务需求日趋个性化，如何打破政府职能机构的条块分割，构建一个一体化的虚拟政府，集成各类信息资源，为公众提供“一站式”的、个性化的服务，是当前电子政务理论和实践的热点课题。本文首先提出一个面向公众的“一站式”服务总体框架；其次，将个性化信息服务的概念引入到电子政务系统中，构建一个个性化信息服务模型；最后，运用数据挖掘技术对模型中的关键技术加以实现。     

基于Web挖掘和站点拓扑的自适应网站研究

针对当前自适应网站实现领域研究的不足,尤其是缺乏对网站自身拓扑结构的分析,站在用户的角度,结合Web日志挖掘和站点拓扑分析,提出地标系数这一指标来衡量网站结点的重要性.在此基础上,提出自适应网站的实现架构,并给出智能推荐(包括高亮显示和动态地图)和缓冲预取两种自适应策略.最后,对一个原型系统进行实验分析.结果表明,提出的自适应策略可提高网站访问者的访问效率.     

Forecasting cancellation rates for services booking revenue management using data mining

Revenue management (RM) enhances the revenues of a company by means of demand-management decisions. An RM system must take into account the possibility that a booking may be canceled, or that a booked customer may fail to show up at the time of service (no-show). We review the Passenger Name Record data mining based cancellation rate forecasting models proposed in the literature, which mainly address the no-show case. Using a real-world dataset, we illustrate how the set of relevant variables to describe cancellation behavior is very different in different stages of the booking horizon, which not only confirms the dynamic aspect of this problem, but will also help revenue managers better understand the drivers of cancellation. Finally, we examine the performance of the state-of-the-art data mining methods when applied to Passenger Name Record based cancellation rate forecasting.     

To ask or not to ask,that is the question

Applicants for credit have to provide information for the risk assessment process. In the current conditions of a saturated consumer lending market, and hence falling “take” rates, can such information be used to assess the probability of a customer accepting the offer?     

DEA based dimensionality reduction for classification problems satisfying strict non-satiety assumption

This study shows how data envelopment analysis (DEA) can be used to reduce vertical dimensionality of certain data mining databases. The study illustrates basic concepts using a real-world graduate admissions decision task. It is well known that cost sensitive mixed integer programming (MIP) problems are NP-complete. This study shows that heuristic solutions for cost sensitive classification problems can be obtained by solving a simple goal programming problem by that reduces the vertical dimension of the original learning dataset. Using simulated datasets and a misclassification cost performance metric, the performance of proposed goal programming heuristic is compared with the extended DEA-discriminant analysis MIP approach. The holdout sample results of our experiments shows that the proposed heuristic approach outperforms the extended DEA-discriminant analysis MIP approach.     

Community detection in social networks based on improved Label Propagation Algorithm and balanced link density

Nowadays, community detection has been raised as one of the key research areas in the online social networks mining. One of the most common algorithms in this field is label propagation algorithm (LPA). Even though the LPA method has advantages such as simplicity in understanding and implementation, as well as linear time complexity, it has an important disadvantage of the uncertainty and instability in outcomes, that is, the algorithm detects and reports different combinations of communities in each run. This problem originates from the nature of random selection in the LPA method. In this paper, a novel method is proposed based on the LPA method and the inherent structure, that is, link density feature, of the input network. The proposed method uses a sensitivity parameter (balance parameter); by choosing the appropriate values for it, the desired qualities of the identified communities can be achieved. The proposed method is called Balanced Link Density-based Label Propagation (BLDLP). In comparison with the basic LPA, the proposed method has an advantage of certainty and stability in the output results, whereas its time complexity is still comparable with the basic LPA and of course lowers than many other approaches. The proposed method has been evaluated on real-world known datasets, such as the Facebook social network and American football clubs, and by comparing it with the basic LPA, the effectiveness of the proposed method in terms of the quality of the communities found and the time complexity has been shown.     

煤炭矿区植被冠层光谱土地复垦敏感性分析

煤矿区土地复垦及复垦监测工作,对于我国土地利用和生态环境治理具有重要意义。微生物复垦技术能够促进植物吸收利用矿质养分和水分,增强土壤肥力,对矿区生态恢复具有显著作用。监测和评价土地复垦效应对植物生长影响的传统方法,通常采用野外采集植物和土壤样本并进行室内分析,但这些方法不仅破坏植物根系原状土壤,造成植株损伤,而且耗费人力、物力,时效性差。高光谱遥感技术具有数据获取速度快、信息量大、精度高且无须离体破坏植株等优点,对于土地复垦监测有非常大的潜力。目前,土地复垦效应遥感监测相关研究仍以观测盆栽大豆、玉米等作物的叶片光谱分析为主。实际上,卫星遥感数据观测到的是冠层光谱,并非叶片光谱,但目前还没有通过植被冠层光谱对矿区土地复垦进行监测的研究成果出现。植被冠层光谱不仅受到叶片光谱的影响,还受到植株长势、下垫面等其他因素的影响,光谱特征变化更为复杂。矿区植被冠层光谱特征对于土地复垦效应的敏感度分析,是对矿区植被理化参量进行定量反演的基础,也是限制高光谱技术应用于大面积土地复垦监测的主要瓶颈。于煤炭矿区土地复垦实验基地开展野外冠层光谱观测实验,获取了接菌组和对照组野外植株冠层光谱数据,并从光谱波形变化和光谱特征参量变化两方面综合分析了植被冠层光谱对土地复垦的敏感性。冠层光谱波形方面,分别采用标准差和光谱敏感度作为组内和组间光谱波形差异的有效指标;冠层光谱特征参量方面,选取了植被红边、黄边、蓝边、绿峰、红谷等典型光谱特征,计算获取其位置、斜率、面积等特征参量,并通过描述性统计和单因素方差分析研究了这些冠层光谱特征参量对土地复垦效应的敏感性,挑选出矿区土地复垦监测的有效特征参量。研究表明,接菌组和对照组冠层光谱的主要波形变化趋势一致,但接菌组植株的生长状况更稳定,不同植株之间差异较小,且绿峰和红谷两个特征更突出。这说明土地复垦能够减少植株间冠层光谱差异,增强植被典型光谱特征,而绿峰和红谷对土地复垦有较高的光谱敏感度。光谱特征参量方面,绿峰、红谷、红边波长在土地复垦作用下显著向长波方向移动,而此前叶片光谱研究中对土地复垦较敏感的红边、蓝边斜率变化并不显著。这说明,野外植被冠层光谱分析结果与实验室植被叶片光谱分析的结果并不完全一致,这可能和植被类型、生长周期、土壤背景光谱干扰等因素相关。在采用卫星或航拍遥感数据进行矿区植被环境监测时,所获取的都是植被冠层光谱,因此本研究所得到的结论具有更强的参考意义和实际应用价值。     

Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.