查询词典 data mining

During the updating and mining of TCIS-Tree several optimal strategies, such as data filtering, heuristically and dynamically adjusting the pruning threshold and mining threshold, and so on, have been explored to make our method efficient.

在TCIS-Tree的更新和挖掘过程中，采用数据过滤、启发式动态调整剪枝阈值、挖掘阈值等若干优化措施，有效地提高算法的时空效率。

It first introduce the destination of data mining, the main techniques, system structure and running process, then describes basic theories and Apriori algorithms of association rules mining particularlly, through analysis and explanation, it refers to confidence and supportance.

它首先介绍了关联规则挖掘研究的现状以及研究的目的和意义，并给出数据挖掘的体系结构和运行过程。

Data mining for traversal patterns has been found useful in several applications. However, traditional model of traversal patterns mining only considered unweighted traversals.

遍历模式数据挖掘方法已经在多种应用中被提出，传统的遍历模式挖掘仅仅考虑了非加权遍历。

Then checking up mining practice, analyzing the observation and prediction data of dripping water amount and effusing water amount throughout mining. It is obvious from the research that the definitive width of waterproof pillar

研究结果表明，所确定的防水煤柱宽度满足开采要求，经济合理；同时，所采用的几种研究方法分析结果基本吻合，说明运用上述方法进行安全防水煤柱宽度的合理留设具有较强的可靠性，对类似矿山有一定的参考价值。

The chief points of this paper are summered as follows: Firstly, the dynamic rule-adjusting algorithms in Snort is analyzed. Secondly, some methods to improve the algorithms are discussed and the results of experiment are obtained. Thirdly, a kind of anomaly detection models using association rule mining algorithm is put forward, and the improvements of Apriori algorithm are pointed out. Fourthly, the advantage of the new algorithm and the feasibility of this model are testified. Lastly, a framework of distributed intrusion detection system based on data mining is designed, and distributed cooperation concepts and many detecting methods are adopted in order to guarantee the accuracy and completeness of the detecting system.

论文的主要工作如下：本文分析了Snort中的动态规则调整算法，讨论了一些改进的思路，给出了实验结果；提出了一种运用关联规则挖掘算法进行异常检测的模型，根据入侵检测的实际情况对Apriori算法进行了改进，通过实验证实了改进算法的优越性和模型的可行性；设计了一种基于数据挖掘的分布式入侵检测系统框架，采用分布式协作思想，结合多种检测方法，保证了系统检测的准确性和完备性。

On the one hand, there are lots of association rules theory of application-oriented is waiting for explore and creative in these big theory framework; on the other hand, with the development of data mining and relevance technology, it must will birth new mining theory, and it will have impetus to some particular application, it may merge particular application target, such as marketing, custom relationship managements electronic business.

一方面，在这些大的理论框架下有许多面向实际应用目标的关联规则挖掘理论等待探索和创新；另一方面，随着数据挖掘技术本身和相关技术的发展，新的挖掘理论的诞生是必然的，而且可能对特定的应用产生推动作用，可能融合特定的应用目标，如市场营销、CRM、电子商务等。

The text obtains some data that caving span of immediate roof is 30m, caving span of main roof at first weighting is 42m and caving span of main roof at periodic weighting is 14m. Mining influence range is determined about 40m through numerical modeling, namely about three advance pillars were in the range of mining influence.

利用数值模拟得出煤柱采场直接顶的垮落步距为30m，老顶的初次来压步距为42m，老顶的周期来压步距为14m；通过模拟超前支承压力，确定了采动影响范围约为40m，即采场超前的三个煤柱都在影响之内。

At last,presents a modem long - distance education model which establishes on the basis of Web mining and data mining .

在Web挖掘和数据挖掘研究的基础上，提出了一个智能化、个性化的现代远程教育系统结构模型。

It is a orient-subject, integrating conformance gather and come into being by processing data in cleanout, change, loading. It supports integrate, uniform, accurate data for data mining.

数据仓库是一个面向主题的，集成的，时变的，非易失的数据集合，它是将各种数据通过清洗、转换、装载过程而形成的，它为数据挖掘提供完整、统一、准确的数据。

Among used machine learning methods, the gradient descent method is widely used to train various classifiers, such as Back-propagation neural network and linear text classifier. However, the gradient descent method is easily trapped into a local minimum and slowly converges. Thus, this study presents a gradient forecasting search method based on prediction methods to enhance the performance of the gradient descent method in order to develop a more efficient and precise machine learning method for Web mining.However, a prediction method with few sample data items and precise forecasting ability is a key issue to the gradient forecasting search method. Applying statistic-based prediction methods to implement GFSM is unsuitable because they require a large number of data items to model a prediction model. In the contrast with statistic-based prediction methods, GM(1,1) grey prediction model does not need a large number of data items to build a prediction model, and it has low computational load. However, the original GM(1,1) grey prediction model uses a mathematical hypothesis and approximation to transform a continuous differential equation into a discrete difference equation in order to model a forecasting model.

其中梯度法是一个最常被使用来实现机器学习的方法之一，然而梯度法具有学习速度慢以及容易陷入局部最佳解的缺点，因此，本研究提出一个梯度预测搜寻法则(gradient forecasting search method， GFSM)来改善传统梯度法的缺点，用来提升一些以梯度学习法则为基础的分类器在资讯探勘上的效率与正确性；而一个所需资料量少、计算复杂度低且精确的预测模型是梯度预测搜寻法能否有效进行最佳解搜寻之关键因素，传统统计为基础之预测方法的缺点是需要较大量的数据进行预测，因此计算复杂度高，灰色预测模型具有建模资料少且计算复杂度低等优点，然而灰色预测理论以连续之微分方程式为基础，并且透过一些数学上的假设与近似，将连续之微分方程式转换成离散之差分方程式来对离散型资料进行建模及预测，这样的作法不尽合理，且缺乏数学理论上的完备性，因为在转换过程中已经造成建模上的误差，且建模过程仅考虑相邻的两个资料点关系，无法正确反应数列未来的变化趋势。

Don not attempt to do something which you can not to do.

不要企图做那些办不到的事情。

The expression of CTGF and TNF-αweredetected by immunochemistry and the number of Clara Cells was calculated.

光镜下观察肺组织的病理变化，采用免疫组化染色观察肺组织中结缔组织生长因子和肿瘤坏死因子-α的表达和Clara细胞的数量。