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In the research of data mining algorithm, the more influential one is associational rule finding algorithm.

在数据挖掘算法的研究中,比较有影响的是关联规则发现算法,它是数据挖掘研究的一个重要分支,也是数据挖掘的众多知识类型中最为典型的一种。

Introduced Fan Boolean Algebra theory as a whole, after analyzing and studying of it, concluded that: the characteristic of Fan Boolean Algebra has determined that it can solve the extant problem of Data Mining to a certain extent, for example, it can guarantee the result after data mining to be usable , assured and construable;can solve the problems about expression difficulty of complicated concept, correlation of attributes emphasized incompletely and redundant examining;it can set up the unified model of Fan Boolean in a certain system;and it can promote the developmental research of new decision support system.

对泛布尔代数进行总体介绍,在分析和研究它的理论体系后,概括出:它的特性决定了它能在一定程度上解决数据挖掘的现存问题,比如可以保证数据挖掘结果的可用性、确定性及可解释性,可以解决复杂概念表达困难、属性间的相互关系强调不够、重复检验等问题;能就某一系统建立统一的泛布尔模型;并促进新型决策支持系统的开发研究,并对泛布尔代数与数据挖掘相结合的原理展开论述。

Features of the variety and dynamitic of data source breaks through the restriction of traditional data mining, and with the development of web, web mining gets more and more attention and

自适应站点是Web挖掘的一个应用分支,通过Web数据挖掘技术挖掘用户使用站点的有用信息,自动调整站点结构和表现形式,从而满足不同用户

Association rule is an important content in data mining, efficient arithmetic is the most important part of data-mining 昺odel, especially when facing the complicated and various demands, it is necessary to propose characteristic and complementary arithmetic according to different factors. The paper analyses elaborately the idea of association rule mining arithmetic based on constraints, studies the classes of constraints in order to confirm its utility. The arithmetic aiming at united variable constraints improvement strategy and exchange of complication aggregation constraints has been designed, the practice has proved that it is effective.

关联规则是数据挖掘研究的重要内容,高效的算法是数据挖掘的重要组成部分,而且面对复杂的多方面的需求,应提供针对各种因素的各具特色的互补的多种算法,本文详尽地研究了基于约束的关联规则挖掘算法的思想,深入地研究了约束的分类、确定其可利用的性质,在此基础上提出了针对联合约束中前驱和后继重叠时的改进策略和复杂聚集约束的转换方法。

The major achievement of this paper is: Based on characteristics of the traffic data distribution, execute pattern recognition operations on traffic condition on two dimensions by clustering, then use BP neural network to describe and forecast traffic flow aiming at each pattern. Making use of classic flow-occupancy inverse "V" model, implement polynomial fitting using least-squares algorithm and statistics method on flow curves to detect outliers which are proved to be not accord with practice through the actual implement, then use the moving average model to recorrect the outliers and absent. Make correlation analysis on muti-direction flow queues of the intersection and ones of upriver intersections, choose flow queue with high correlation as assistant one to improve the error tolerance of the prediction system, at the same time we can use the method to give an estimation of flow in intersection with out sensors. We design and implement an SOA(Service-Oriented Architecture)-based UTDD(urban traffic data mining development) with high expansibility and performance, which implement unified management and call of the data-mining application though defining a XML-based description of data-mining process and a common interface to call data-mining process, finally we build traffic flow prediction application model on UTDD.

根据交通流量数据分布的特征,提出基于k-means的二次聚类方法,对交通流量在流量大小和时间上进行模式划分,进而对各个交通流模式进行基于BP神经网络的描述和预测,从而提高模型对流量预测的精度; 2)根据流量/时间占有率倒&V&字形曲线分布模型,提出基于最小二乘法的三次多项式曲线拟合和统计方法的异常检测方法,实际应用表明该方法能够有效识别异常数据,然后根据移动平均算法对异常数据进行修正; 3)基于序列相关性分析,分别对预测方向的交通流量数据序列、上游路口相关序列以及预测路口其它各个方向上的交通流量序列进行分析,选择相似性流量序列,作为辅助序列提供其他没有检测器路口的流量估计; 4)设计和实现了基于SOA(Service-Oriented Achitecture)的高性能、可扩展的智能交通数据挖掘系统UTDD,该系统通过定义基于XML的数据挖掘过程描述和通用的过程模型接口,实现数据挖掘应用的统一管理和调用,最后在UTDD上建立了基于路口流量预测的应用模型。

In this algorithm,by constructing a UserlD-URL revelant matrix similar customer groups are discovered by measuring similarity between column vectors and relevant web pages are obtained by measuring similarity between row vectors;frequent access paths can also be discovered by further processing of the latter.

第四是本文将传统数据挖掘过程中的各种关键技术,引入到对Web使用信息的挖掘活动中,结合关系数据库的特点设计并实现了一个具有可广西人学颀士学位论义视化功能的Web使用挖掘系统WLGMS。

Data mining technology has become riper by the research of ten and more years.

而经过十多年的研究,数据挖掘技术已较为成熟,因此近年来研究的重点转为挖掘技术的应用,商务作为数据挖掘的主要应用领域,对知识的需求尤为显著。

To study the customer behavior by carrying out Customer Relationship Management in enterprises, we use some relative technology and method of decision tree of data mining to propose a kind of UPTree algorithm of data Mining, which is used to mine a great deal of information hidden in customer behavior.

为了解决在企业中实施客户关系管理,CRM系统中客户行为的定量研究问题,利用决策树的数据挖掘相关技术和方法,提出了UPTree数据挖掘算法,并采用UPTree算法对隐藏在大量客户行为中的信息进行挖掘,从而获取了CRM系统中潜在的客户行为规则,并给出这些行为规则的IF-THEN的描述形式,为企业的科学决策提供依据。

Part Ⅰintroduces the development and research status of data mining, anglicizing the application prospect of data mining technique in mobile network analysis, explaining in detail the problems that should be solved in analysis of mobile network based on the data mining.

介绍了数据挖掘的发展与研究现状,分析数据挖掘技术在移动网络分析中的应用前景,具体介绍了基于数据挖掘的移动网络分析应解决的问题。2。

Pesky armadillos, they found, can move artifacts in archaeological dig sites up, down and even laterally by several meters as they dig.

他们发现讨厌的犰狳会移动考古挖掘现场的手工艺品,在他们挖掘时,这些犰狳有时会把东西搬到上面的地层,有时是下面的地层,甚至搬到离挖掘地点几米远的地方。

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