可挖掘的

The article first discusses the related concepts and basic method of data mining. The task of data mining is to find mode from database. The mode can be many , we can dispart two according to function: predictive mode and descriptive mode.

文章首先论述了数据挖掘的相关概念以及数据挖掘的基本方法，数据挖掘的任务是从数据集中发现模式，模式可以有很多种，按功能可分为两大类：预测型模式和描述型模式。

In order to provide a flexible and patulous calculating platform and execute high efficiency data mining, a calculating architecture and algorithms of data mining are presented to apply in distributed and parallel environment.

为了提供一个灵活可扩展的计算平台进行高效的挖掘计算，提出了一种应用于分布和并行环境的数据挖掘计算框架和相应的算法。

Algorithm overcame the limitation that digs algorithm at present, mining flow structure while mining management handles action, strengthened process mining applicably quality.

算法克服了目前挖掘算法的限制，挖掘流程结构的同时挖掘管理操作行为，加强了过程挖掘的可适用性。

The γ algorithm overcame the limits of current mining algorithm, and it could mine management operation actions while mining the process structure, which greatly improved the applicatory of process mining.

算法克服了目前挖掘算法的限制，挖掘流程结构的同时挖掘管理操作行为，加强了过程挖掘的可适用性。

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。

Based on the basic concepts of data mining , this dissertation compares and analyses the differences of data mining and other methods such as KDD and OLAP , classifies and summarizes the objects of data mining , the findable patterns and the common techniques in detail .

在介绍数据挖掘基本概念的基础上，对数据挖掘与传统分析方法，数据库中的知识发现和联机分析处理做了深入地分析和比较，对数据挖掘的对象，可发现的模式进行了详细地分类，归纳和总结，对数据挖掘常使用的技术做了介绍和分析。

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。

As an "unlimited" spending, the economic potential of tour-shopping is the most part in the inscape of tour products, thus many developed countries and areas in the world attach importance to tour-shopping.

旅游购物作为&无限&花费，在旅游产品的构成要素中可挖掘的经济效益的潜力最大，因此世界上许多旅游业发达国家和地区都十分重视发展旅游购物。

A design of reconfigurable data mining systematic framework is proposed to improve speed and precision.

为了提高数据挖掘的速度和精度，提出了可重构的数据挖掘系统框架设计方案。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。