基于网络环境的分布式KDD及Data Mining研究

本文针对ＫＤＤ的研究现状及其面临的挑战,主要讨论了基于网络环境下,面向多个站点机,多种数据库、多类数据源的分布式ＫＤＤ和Ｄａｔａ Ｍｉｎｉｎｇ的整体方案和实验系统模型,研究内容包括高效分布式开采算法,ＫＤＤ过程的无缝集成,ＫＤＤ中的知识表示、知识更新以及开采结果可视化的有效方法等。     

利用抽样技术分布式开采可变精度的关联规则

关联规则是数据开采的重要研究内容,利用抽样及元学习技术提出一种快速的分布式开采可变精度的关联规则算法。为了能获得更准确的结果,还给出 采用适当缩小量小支持度和扩大全局检测的候选项集等技术的若干改进算法,最后给出了这种方法与类似方法的比较情况,算法具有效率高和通信量小的特点,尤适合效率比准确性要求更高的场合。     

基于频繁模式树的分布式约束性关联规则挖掘算法研究

在分布式环境中挖掘约束性关联规则是当前研究的热点问题之一。该文在FP-growth算法的基础上,提出了一种新的分布式挖掘约束性关联规则算法DAMICFP。该算法对于解决分布式挖掘约束性关联规则的问题是十分有效的。     

分布式环境下挖掘约束性关联规则的算法研究

关联规则是数据挖掘的重要研究内容。基于约束的关联规则挖掘可以促进交互式探查与分析。该文主要研究了分布式环境中挖掘约束性关联规则的问题。在并行关联规则挖掘算法CD和约束性关联规则挖掘算法Direct的基础上,提出了一种新的分布式挖掘约束性关联规则算法DMA_IC。该算法对于解决分布式挖掘约束性关联规则的问题是十分有效的。同时,文章还对DMA_IC算法的通信性能进行了讨论。     

典型关联规则挖掘算法的分析与比较

关联规则的发现是数据挖掘的一个重要方面,目前许多研究人员正致力于关联规则的快速开采算法的研究。文中介绍了几种典型的开采大型事务数据库中所有关联规则的算法,特别针对算法过程中产生候选频繁项集的大小和所需扫描事务数据库的次数这两个影响关联规则挖掘效率的关键问题,分析各个算法采用的解决策略及相应的局限性,并比较它们的时间效率和空间效率。最后展望了关联规则挖掘算法的研究方向。     

用数据查询语言实现关联规则脂掘

关联规则采掘具有广阔的应用前景。文章讨论了关联规则的用途,内涵,分析了关联规则采掘的算法,给出了在ＤＢＭＳ平台上用数据查询语言实现关联规则采掘的程序。测试表明,实现的算法对关联规则采掘十分有效。     

快速多层次关联规则的挖掘

知识发现是指对原始数据进行分析，提取出隐含的，有用的规则，是当前快速发展的研究领域，是知识获取的重要方法，关联规则是知识发现的重要研究内容之一，本文提出了一种新的多层次关联规则挖掘算法ＭＬ＿ＡＲ，算法ＭＬ＿ＡＲ在挖掘过程中，只对最低概括层次上的候选系模式进行模式的匹配计算，求解出简化的频繁式集合，最后再求解各个概括层次上的繁频模式集合，算法ＭＬ＿ＡＲ有效地利用了概括的层次关系，减少了模式的匹配计算     

典型关联规则挖掘算法的分析与比较

关联规则的发现是数据挖掘的一个重要方面，目前许多研究人员正致力于关联规则的快速开采算法的研究。文中介绍了几种典型的开采大型事务数据库中所有关联规则的算法，特别针对算法过程中产生候选频繁项集的大小和所需扫描事务数据库的次数这两个影响关联规则挖掘效率的关键问题，分析各个算法采用的解决策略及相应的局限性，并比较它们的时间效率和空间效率。最后展望了关联规则挖掘算法的研究方向。     

抛物型微分方程组初边值问题的有限元算法

抛物型微分方程组初边值问题的有限元算法王同科（河南师范大学数学系）ＦＩＮＩＴＥＥＬＥＭＥＮＴＭＥＴＨＯＤＦＯＲＰＡＲＡＢＯＬＩＣＤＩＦＦＥＲＥＮＴＩＡＬＥＱＵＡＴＩＯＮＳＷＩＴＨＩＮＩＴＩＡＬＡＮＤＢＯＵＮＤＡＲＹＶＡＬＵＥＰＲＯＢＬＥＭＳ￥Ｗａｎｇ...     

一种适用于UML语言的软件开发过程MFDP

本文概要介绍了一种适用于最新的面向对象可视化建模语言ＵＭＬ的软件开发过程ＭＦＤＰ的组织结构，剖析了ＭＦＤＰ的主要特点，研究了ＵＭＬ在ＭＦＤＰ各个阶段的应用。本文旨在将国外软件工程领域最新的研究成果引进到国内并推动代表软件开发技术发展主流方向的可视化建模语言ＵＭＬ在我国的推广和使用。     

Mining the optimal class association rule set

We define an optimal class association rule set to be the minimum rule set with the same predictive power of the complete class association rule set. Using this rule set instead of the complete class association rule set we can avoid redundant computation that would otherwise be required for mining predictive association rules and hence improve the efficiency of the mining process significantly. We present an efficient algorithm for mining the optimal class association rule set using an upward closure property of pruning weak rules before they are actually generated. We have implemented the algorithm and our experimental results show that our algorithm generates the optimal class association rule set, whose size is smaller than 1/17 of the complete class association rule set on average, in significantly less rime than generating the complete class association rule set. Our proposed criterion has been shown very effective for pruning weak rules in dense databases.     

基于分类修剪的关联分类算法改进

针对现有关联分类算法资源消耗大、规则剪枝难、分类模型复杂的缺陷,提出了一种基于分类修剪的关联分类算法改进方案ACCP.根据分类属性值的不同对分类规则前项进行分块挖掘,并对频繁项集挖掘过程和规则修剪进行了改进,有效提高了分类准确率和算法运行效率.实验结果表明,此算法改进方案相比传统CBA算法和C4.5决策树算法有着更高的分类准确率,取得了较好的应用效果.     

基于粗糙集理论的ORD关联规则挖掘算法

关联规则挖掘是数据挖掘的重要领域之一,利用粗糙集理论来挖掘关联规则的方法已经得到广泛关注.针对不完备信息系统,提出了基于粗糙集理论的快速ORD关联规则挖掘算法.该算法首先采用基于粗糙集理论的属性约简算法进行属性约简,然后采用快速、高效的冗余项集和冗余规则修剪算法--ORD算法获取关联规则.将该算法与其它同类流行的算法在4个UCI数据集上进行实验比较,结果表明该算法性能良好.     

基于IF-THEN规则的决策树裁剪算法

决策树是机器学习和数据挖掘领域中一种基本的学习方法。文中分析了C4.5算法以及该算法不足之处,提出了一种决策树裁剪算法,其中以规则信息量作为判断标准。实验结果表明这种方法可以提高最终模型的预测精度,并能够很好克服数据中的噪音。     

一种基于多维集的关联模式挖掘算法

大多数维间关联规则挖掘算法如基于数据立方体的关联规则挖掘算法都假定对象的属性取值只具有单值性．将对象的属性取值扩展到多值，据此提出多维集的概念和基于多维集关联规则的语义特征．在此语义特征下，提出了一个多维集的关联规则挖掘算法．该算法利用多维集关联规则的限制特征，能够在数据集缩减的同时进行侯选集的三重剪枝，因此，具有比直接使用apriori等算法更好的性能，分析了算法的性能和正确性、完备性，并通过实验对算法有效性进行了对比．     

Levelwise Search and Pruning Strategies for First-Order Hypothesis Spaces

The discovery of interesting patterns in relational databases is an important data mining task. This paper is concerned with the development of a search algorithm for first-order hypothesis spaces adopting an important pruning technique (termed subset pruning here) from association rule mining in a first-order setting. The basic search algorithm is extended by so-called requires and excludes constraints allowing to declare prior knowledge about the data, such as mutual exclusion or generalization relationships among attributes, so that it can be exploited for further structuring and restricting the search space. Furthermore, it is illustrated how to process taxonomies and numerical attributes in the search algorithm.Several task settings using different interestingness criteria and search modes with corresponding pruning criteria are described. Three settings serve as test beds for evaluation of the proposed approach. The experimental evaluation shows that the impact of subset pruning is significant, since it reduces the number of hypothesis evaluations in many cases by about 50%. The impact of generalization relationships is shown to be less effective in our experimental set-up.     

Induction and pruning of classification rules for prediction of microseismic hazards in coal mines

The paper presents results of application of a rule induction and pruning algorithm for classification of a microseismic hazard sate in coal mines. Due to imbalanced distribution of examples describing states “hazardous” and “safe”, the special algorithm was used for induction and rule pruning. The algorithm selects optimal parameters‘ values influencing rule induction and pruning based on training and tuning sets. A rule quality measure which decides about a form and classification abilities of rules that are induced is the basic parameter of the algorithm. The specificity and sensitivity of a classifier were used to evaluate its quality. Conducted tests show that the admitted method of rules induction and classifier’s quality evaluation enables to get better results of classification of microseismic hazards than by methods currently used in mining practice. Results obtained by the rules-based classifier were also compared with results got by a decision tree induction algorithm and by a neuro-fuzzy system.     

基于神经网络与遗传算法的数据挖掘体系结构

从神经网络中提取规则可以有效地应用于数据挖掘中的分类问题。作为一种有效的优化方法，遗传算法可以应用于规则剪枝。提出了一个基于神经网络与遗传算法的数据挖掘体系结构，可以应用于数据挖掘中的分类问题。     

Classification based on association rules: A lattice-based approach

Classification plays an important role in decision support systems. A lot of methods for mining classification rules have been developed in recent years, such as C4.5 and ILA. These methods are, however, based on heuristics and greedy approaches to generate rule sets that are either too general or too overfitting for a given dataset. They thus often yield high error ratios. Recently, a new method for classification from data mining, called the Classification Based on Associations (CBA), has been proposed for mining class-association rules (CARs). This method has more advantages than the heuristic and greedy methods in that the former could easily remove noise, and the accuracy is thus higher. It can additionally generate a rule set that is more complete than C4.5 and ILA. One of the weaknesses of mining CARs is that it consumes more time than C4.5 and ILA because it has to check its generated rule with the set of the other rules. We thus propose an efficient pruning approach to build a classifier quickly. Firstly, we design a lattice structure and propose an algorithm for fast mining CARs using this lattice. Secondly, we develop some theorems and propose an algorithm for pruning redundant rules quickly based on these theorems. Experimental results also show that the proposed approach is more efficient than those used previously.     

基于动态剪枝的关联规则挖掘算法

介绍了目前关联规则挖掘的研究工作 .分两个部分提出了基于动态剪枝的关联规则发现方法 .讨论了如何实施动态剪枝 ,给出了一个基于三元组结构的树式存储结构 ,在此基础上描述了交易数据库中知识发现算法 .并将提出的方法与关联规则挖掘中具有里程碑意义的 Apriori算法进行了对比分析 ,给出了相应的分析结果 ,实验表明该方法能有效地从数据集中发现关联规则