基于关联规则兴趣度的课程设置研究

介绍数据挖掘和关联规则的概念,引入一个关联规则新的度量值——兴趣度,并使用Visual FoxPro开发了一个关联规则挖掘系统。在设定最小支持度、最小置信度和兴趣度的条件下,使用挖掘系统对计算机专业学生的专业课成绩进行关联分析,通过分析找出它们间的内在联系,为课程设置提供依据。     

关联规则算法在中文文本挖掘中的应用研究

本文介绍了关联规则的主要概念及关联规则的经典算法,并将关联规则算法应用于中文文本挖掘中,目的是通过计算文本特征词间的支持度、可信度关系了解文本间的关联关系.     

基于Rough集的单维布尔关联规则的挖掘算法

利用Rough集理论中关于等价类的概念,提出了单维布尔关联规则问题挖掘算法,考虑到关联规则设定单一最小支持度阈值的局限性,提出使用多个最小支持度的办法进行频繁项集的发现,利用兴趣度对单维布尔关联规则进行评价.     

兴趣关联规则的挖掘

通过对关联规则兴趣度的度量,在挖掘关联规则时可以避免无意义规则的产生。提出了一种度量关联规则兴趣度的方法,并给出了兴趣关联规则的挖掘算法。     

基于关联规则的道路交通事故多发点成因分析

分析道路交通事故成因有助于改善道路交通环境、减少道路交通事故的发生.近年来,关联规则模型及其扩展在道路交通事故多发点成因分析中使用广泛.然而,使用关联规则中传统的兴趣度度量方法和Apriori算法存在许多局限.在传统兴趣度度量方法和Apriori算法的基础上做出改进,使用频数分析法和改进的关联规则方法对事故多发点进行成...     

基于SOFM网络的多层模糊关联规则挖掘算法

为了表示复杂庞大的概念层次树,文中提出了一种更加通用的编码方案,将概念分层应用于模糊关联规则的挖掘.此外,为解决隶属度函数难以主观确定的问题,引入一种SOFM网络来确定样本数据的隶属度函数.基于改进的概念层次树的编码方案和SOFM网络,将模糊集引入关联规则挖掘中,设计了一种新的多层模糊关联规则挖掘算法.实验结果表明,该算法可以有效地挖掘出易于理解的、有意义的多层次模糊关联规则,具有很好的效率和伸缩性.     

基于联合信息熵和粗糙集理论的关联知识发现

研究了基于联合熵和粗糙集理论的关联规则挖掘算法,改进了基于粗糙集的属性离散化方法—连续属性联合熵差离散化算法;以联合信息熵作为属性约简的标准,提出了基于联合熵的知识约简算法;并给出了以支持度、兴趣度和准确度为阈值的有效关联规则算法.     

基于Apriori算法的高职院校课程相关性分析

高职院校的人才培养方案中,课程设置是最关键的元素,课程结构的合理与否会直接影响到人才培养的质量。使用关联规则中的Apriori算法,对学生成绩样本数据进行挖掘,利用给定的最小支持度和最小置信度,挖掘出频繁项集,进行课程相关性分析,得到课程的关联规则,有利于在课程设置过程中优化课程结构,提高教学质量。     

兴趣模糊关联规则挖掘现状研究

模糊关联规则是通过给定最小支持率和最小信任度来得到很多关联规则的,这些规则之间大部分是显而易见或不相关,为有效得到用户关注的模糊关联规则,引用了兴趣度了的概念。本文主要针对目前兴趣模糊关联规则挖掘现状进行分析研究。     

基于兴趣度的Apriori算法在公共计算机情况分析中的应用

本文将关联规则Apriori算法应用到高校新生公共计算机情况分析中,且在利用关联规则Apriori算法的基础上,加入兴趣度的优化算法,最终挖掘出有趣的关联规则,从而指导高校公共计算机教学。     

挖掘关注的语言值关联规则

为了解决利用RFCM算法划分数量型属性，并通过组合语言值进行语言关联规则挖掘中出现的规则数量太多，以及难于获得用户真正关注的规则等问题，提出了一种改进的语言值关联规则挖掘算法。通过最大隶属原则将记录在数量型属性上的取值转换为语言值，然后转换成布尔型属性关联规则挖掘问题。同时，给出一个能够度量语言值关联则简洁性和新奇性关注程度（兴趣度）的计算函数，用于减少选取关注语言值关联规则的工作量。采用本文提出的方法对一组实例数据进行实验，得到了关注程度较高的语言值关联规则。所采用的方法能适用于含有大量数量型属性的数据库，并能有效地获取用户关注的规则。     

Research on spatial association rules mining in two direction

In data mining from transaction DB, the relationships between the attributes have been focused, but the relationships between the tuples have not been taken into account. In spatial database, there are relationships between the attributes and the tuples, and most of the associations occur between the tuples, such as adjacent, intersection, overlap and other topological relationships. So the tasks of spatial data association rules mining include mining the relationships between attributes of spatial objects, which are called as vertical direction DM, and the relationships between the tuples, which are called as horizontal direction DM. This paper analyzes the storage models of spatial data, uses for reference the technologies of data mining in transaction DB, defines the spatial data association rule, including vertical direction association rule, horizontal direction association rule and twodirection association rule, discusses the measurement of spatial association rule interestingness, and puts forward the work flows of spatial association rule data mining. During twodirection spatial association rules mining, an algorithm is proposed to get nonspatial itemsets. By virtue of spatial analysis, the spatial relations were transferred into nonspatial associations and the nonspatial itemsets were gotten. Based on the nonspatial itemsets, the Apriori algorithm or other algorithms could be used to get the frequent itemsets and then the spatial association rules come into being. Using spatial DB, the spatial association rules were gotten to validate the algorithm, and the test results show that this algorithm is efficient and can mine the interesting spatial rules.     

Research on spatial association rules mining in two-direction

In data mining from transaction DB, the relationships between the attributes have been focused, but the relationships between the tuples have not been taken into account. In spatial database, there are relationships between the attributes and the tuples, and most of the associations occur between the tuples, such as adjacent, intersection, overlap and other topological relationships. So the tasks of spatial data association rules mining include mining the relationships between attributes of spatial objects, which are called as vertical direction DM, and the relationships between the tuples, which are called as horizontal direction DM. This paper analyzes the storage models of spatial data, uses for reference the technologies of data mining in transaction DB, defines the spatial data association rule, including vertical direction association rule, horizontal direction association rule and two-direction association rule, discusses the measurement of spatial association rule interestingness, and puts forward the work flows of spatial association rule data mining. During two-direction spatial association rules mining, an algorithm is proposed to get non-spatial itemsets. By virtue of spatial analysis, the spatial relations were transferred into non-spatial associations and the non-spatial itemsets were gotten. Based on the non-spatial itemsets, the Apriori algorithm or other algorithms could be used to get the frequent itemsets and then the spatial association rules come into being. Using spatial DB, the spatial association rules were gotten to validate the algorithm, and the test results show that this algorithm is efficient and can mine the interesting spatial rules.     

基于关联规则的数据挖掘研究

本文介绍了关联规则的概念,并通过一个例子说明了关联规则挖掘的一种算法－－Apriori算法,指出了数据挖掘未来研究的重点和方向。     

高职院校路桥专业实践性课程考核方法改革研究

分析了高职院校路桥专业课程考核方法存在的缺点,以《道路工程测量》课程为例,提出了实践性较强课程的课程考核方案,以期为高职院校路桥专业的课程考核方法改革提供依据。     

高职院校专业课程教学过程整体设计的研究

从专业课程的定位、专业课程的教学设计和专业课程教学特色等方面研究了高职教育的教学过程整体设计,提出了高职专业课程标准的新理念,并强调教学实施中要突出＂能力为本＂的职业专业课程特色。     

论高职服装设计专业应用型课程设置改革

针对当前高职服装设计专业应用型课程缺乏的问题,本文主要从实用性,创新等原则论证了如何改革。     

大型数据库中多层次相联规则的提取

针对相联规则的提取,给出算法ＸＬ－Ｔ２Ｌ１。利用概念层次树编码及自顶向下逐步深化的策略发现任意层次概念间的关联,并引入了有趣规则的概念,对已发现的大量规则进行精减,便于用户对规则的利用。