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1.
提出了一种挖掘量化关联规则的MQAR算法。此算法在挖掘关联规则时,只需扫描事务数据库一遍,提高了数据挖掘的效率;并且存放辅助信息所占的内存空间大大少于现有的挖掘算法;同时此算法不仅能挖掘出有关联的数据项集,还能找出这些项集之间数量上的相互关系。  相似文献   

2.
空间关联规则的双向挖掘   总被引:9,自引:0,他引:9  
空间数据库中关联规则挖掘不仅需要考虑关系元组属性之间的关系——纵向关系,更需要挖掘元组之间的关系——横向关系,如相邻、相交、重叠等。本文通过分析空间数据库的存储模式,借鉴事务数据库关联规则的挖掘方法,对空间关联规则进行完整定义,并对规则的兴趣度度量进行探讨。根据挖掘的方向将空间数据挖掘归纳为纵向挖掘、横向挖掘、双向挖掘。在双向挖掘中,提出一种新算法,该算法根据挖掘任务进行约束,缩小挖掘空间,然后通过空间计算将空间关系转化为非空间关系,经过多次循环,获取非空间项集,进而挖掘出空间关联规则。据此提出空间数据双向挖掘工作流程,并通过实例进行了验证。  相似文献   

3.
基于聚类和关联规则的挖掘算法   总被引:4,自引:0,他引:4       下载免费PDF全文
数据挖掘技术中关联规则可以很好地发现数据项之间存在的相互关系,同时有大量的挖掘算法可供选择。聚类分析就是通过分析数据库中的记录数据,根据一定的分类规则合理地划分记录集合,确定每个记录所在类别。它所采用的分类规则是由聚类分析工具决定的。本文研究 聚类和关联规则的挖掘算法。  相似文献   

4.
时态关联规则挖掘是针对在一段时间范围内的关联挖掘,在现实中有较多的应用。现有的大多数时态关联挖掘算法或者需要多次扫描数据库,或者没有考虑各个项在数据集上出现或结束时间上的不同,因而挖掘性能受到较大的制约。为此,本文提出一种增量式的面向具有不同时间出现与结束的项的时态关联规则挖掘算法。为减少存储方面的开销,只需保存已挖掘过的历史数据集中的频繁1项集。为了减少数据的扫描量,通过有效的剪枝策略,有选择性地扫描相关事务项,至多只需扫描一次完整的数据库。实验证明,该算法具有较好的挖掘性能。  相似文献   

5.
徐璐 《福建电脑》2007,(10):88-89
数据挖掘是从海量数据库中挖掘有效或重要信息的过程.普通的关联规则挖掘是数据挖掘的一种基本的方法,但随着时间的推移,挖掘数据库的规模会发生不断变化,人们对数据的需求也会有所不同,如何从扩展数据库中高效地对已经推导出的关联规则进行更新,然后再利用更新的关联规则进行数据挖掘,这就是增量式挖掘关联规则的方法.  相似文献   

6.
随着数据库技术的不断发展及数据库管理系统的广泛应用,大型数据库系统己经在各行各业普及,数据库中存储的数据量急剧增大,数据挖掘便是从海量数据库中挖掘有效或重要信息的过程。关联规则挖掘是数据挖掘领域一个非常重要的研究课题,被广泛地应用于商业界、医疗保险、金融业、电信部门等。随着时间的推移,挖掘数据库的规模会发生不断变化,人们对数据的需求也会有所不同,因此如何从扩展数据库中高效地对已经推导出的关联规则进行更新具有非常重要的应用价值,这就是所谓的增量式挖掘关联规则的问题。  相似文献   

7.
随着大型数据库系统在各行业普及,数据库中存储的数据量急剧增大,数据挖掘是从海量数据中挖掘有效或重要信息的过程。关联规则挖掘作为数据挖掘的重要研究课题,被广泛地应用。伴随挖掘数据库的规模不断发生变化,对数据的需求也会有所不同,如何从扩展数据库中高效地对已经推导出的关联规则进行更新具有非常重要的应用价值,这就是所谓的增量式挖掘关联规则的问题。  相似文献   

8.
关联规则挖掘的研究及对Apriori算法的改进   总被引:11,自引:1,他引:11       下载免费PDF全文
本文对关联规则挖掘问题进行了介绍,并对其作了形式化描述;对Apriori算法进行了分析,针对该算法的缺点,提出改进算法apriori.new。改进的算法避免了模式匹配,减小了访问数据库的次数,从而提高了挖掘速度。  相似文献   

9.
基于综合度量的关联规则挖掘算法   总被引:2,自引:0,他引:2  
陆晶  赛英 《计算机工程》2004,30(22):89-90,131
从确定性,有用性,简洁性和新奇性4个方面对规则进行综合度量,给出了规则长度和兴趣度约束的定义,在传统算法基础上提出了基于综合度量的关联规则挖掘算法,使关联规则的挖掘质量得到提高。  相似文献   

10.
在约束关联规则挖掘过程中,影响交互的制约因素是挖掘算法的执行时间。为了提高挖掘过程的交互性,文章提出一种基于两阶段的约束关联规则挖掘算法。算法利用已挖掘的关联规则,实现约束关联规则的挖掘过程。在算法实现的过程中对关联规则集存储结构进行了优化,并扩展了类SQL查询语句。实验结果表明,由于在约束条件挖掘的过程中不需要再对数据库进行挖掘处理,节省了大量的用户时间,因此算法是有效的。  相似文献   

11.
提出一种新的有别于将时间直接代入关联规则挖掘过程的方法。该方法分别针对确定的有效时间与不确定的有效时间两种情况,通过计算时态数据库中同一对象的元组中指定属性的有效时间之间的同时关系,将所有两两之间具有同时关系的归为一组,从而可以略去元组中的时间因素,并将时态数据库转化为事务数据库,然后即可利用传统挖掘算法进行挖掘。  相似文献   

12.
In this paper, given a set of sequence databases across multiple domains, we aim at mining multi-domain sequential patterns, where a multi-domain sequential pattern is a sequence of events whose occurrence time is within a pre-defined time window. We first propose algorithm Naive in which multiple sequence databases are joined as one sequence database for utilizing traditional sequential pattern mining algorithms (e.g., PrefixSpan). Due to the nature of join operations, algorithm Naive is costly and is developed for comparison purposes. Thus, we propose two algorithms without any join operations for mining multi-domain sequential patterns. Explicitly, algorithm IndividualMine derives sequential patterns in each domain and then iteratively combines sequential patterns among sequence databases of multiple domains to derive candidate multi-domain sequential patterns. However, not all sequential patterns mined in the sequence database of each domain are able to form multi-domain sequential patterns. To avoid the mining cost incurred in algorithm IndividualMine, algorithm PropagatedMine is developed. Algorithm PropagatedMine first performs one sequential pattern mining from one sequence database. In light of sequential patterns mined, algorithm PropagatedMine propagates sequential patterns mined to other sequence databases. Furthermore, sequential patterns mined are represented as a lattice structure for further reducing the number of sequential patterns to be propagated. In addition, we develop some mechanisms to allow some empty sets in multi-domain sequential patterns. Performance of the proposed algorithms is comparatively analyzed and sensitivity analysis is conducted. Experimental results show that by exploring propagation and lattice structures, algorithm PropagatedMine outperforms algorithm IndividualMine in terms of efficiency (i.e., the execution time).  相似文献   

13.
Sequential Pattern Mining in Multi-Databases via Multiple Alignment   总被引:2,自引:0,他引:2  
To efficiently find global patterns from a multi-database, information in each local database must first be mined and summarized at the local level. Then only the summarized information is forwarded to the global mining process. However, conventional sequential pattern mining methods based on support cannot summarize the local information and is ineffective for global pattern mining from multiple data sources. In this paper, we present an alternative local mining approach for finding sequential patterns in the local databases of a multi-database. We propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. Approximate sequential patterns can effectively summerize and represent the local databases by identifying the underlying trends in the data. We present a novel algorithm, ApproxMAP, to mine approximate sequential patterns, called consensus patterns, from large sequence databases in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. We conduct an extensive and systematic performance study over synthetic and real data. The results demonstrate that ApproxMAP is effective and scalable in mining large sequences databases with long patterns. Hence, ApproxMAP can efficiently summarize a local database and reduce the cost for global mining. Furthremore, we present an elegant and uniform model to identify both high vote sequential patterns and exceptional sequential patterns from the collection of these consensus patterns from each local databases.  相似文献   

14.
Many researchers in database and machine learning fields are primarily interested in data mining because it offers opportunities to discover useful information and important relevant patterns in large databases. Most previous studies have shown how binary valued transaction data may be handled. Transaction data in real-world applications usually consist of quantitative values, so designing a sophisticated data-mining algorithm able to deal with various types of data presents a challenge to workers in this research field. In the past, we proposed a fuzzy data-mining algorithm to find association rules. Since sequential patterns are also very important for real-world applications, this paper thus focuses on finding fuzzy sequential patterns from quantitative data. A new mining algorithm is proposed, which integrates the fuzzy-set concepts and the AprioriAll algorithm. It first transforms quantitative values in transactions into linguistic terms, then filters them to find sequential patterns by modifying the AprioriAll mining algorithm. Each quantitative item uses only the linguistic term with the maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as the number of the original items. The patterns mined out thus exhibit the sequential quantitative regularity in databases and can be used to provide some suggestions to appropriate supervisors.  相似文献   

15.
提出了一种针对大型数据库、关于多频项集、动态增最式的挖掘新算法,利用前次的挖掘结果和新增物品项ID的明细数据,能有效地挖掘出频繁项集及各项ID之间的量化比例关系,给商家和物流系统提供信息指导,避免错误决策,对实现物流系统自动化及其它数据挖掘应用领域都具有极其重要的指导意义。  相似文献   

16.
电力调度数据挖掘后处理方法的研究   总被引:1,自引:0,他引:1  
关联规则是数据依赖关系的有效描述方法,是知识发现研究的重要内容.然而,随着所挖掘数据库规模的增大,由传统数据挖掘算法所生成的大量关联规则常常令用户的使用与分析十分困难.文中提出了一种新方法来解决这个问题并将其运用到电力调度数据挖掘系统中.实验结果表明,该方法消除了大量冗余规则,并且使用户可以从整体上把握整个规则集,提高了关联规则挖掘的准确性和易用性.  相似文献   

17.
An implication rule Q → R is a statement of the form "for all objects in the database, if an object has the attribute–value pairs Q then it has also the attribute–value pairs R ." This simple type of rule is theoretically interesting, because it supports reasoning, similar to functional dependencies in database theory, and it may be of practical significance because the size of the set of implication rules that hold in a relation can remain substantially high even when mining real data and considering only most general covers; i.e., covers containing rules with unredundant right and left sizes. Motivated by these observations, we focus on the extraction of short-rule covers, which cannot be efficiently mined by standard rule miners. We present an algorithm driven by "negative examples" (i.e., satisfy Q but not R ) to prune the rule-candidate lattice associated with each "positive example" (i.e., satisfies both Q and R ). The algorithm scales up quite well with respect to the number of objects and it is particularly suitable for databases with attributes described by large domains. Furthermore, a perfect hash function ensures extraction of short-rule covers even from databases containing a large number of attributes.  相似文献   

18.
OSAF-tree--可迭代的移动序列模式挖掘及增量更新方法   总被引:1,自引:0,他引:1  
移动通信技术和无限定位技术的发展积累了海量的、动态增长的时空数据.利用数据挖掘技术从移动用户的时空行为轨迹当中挖掘用户移动序列模式,在移动通信、交通管理、基于位置服务等领域有着广泛的应用前景.由于移动环境网络资源珍贵、数据量大的特点,传统的序列模式挖掘方法在效率上很难满足需求.OSAF-tree算法基于投影的概念,只需要对数据库进行一遍扫描,就可以很好地处理移动序列模式的挖掘及其增量更新和迭代挖掘问题,这是一个非常高效的算法.与已有的方法相比,OSAF-tree算法在性能和I/O代价等方面都具有明显的优势.  相似文献   

19.
分析时态约束关联规则挖掘的TCAR算法,针对其在挖掘频繁时态项集时效率较低的问题,提出一种基于时态约束的关联规则挖掘算法。该算法对源数据库进行处理,缩减存储空间,并在更新挖掘最大频繁项集算法基础上,挖掘最大频繁时态项集。实例分析结果表明,该算法能提高整体挖掘效率。  相似文献   

20.
部署蜜网(Honeynet)的目的之一就是收集数据,但若无法对捕获的数据进行分析处理,则该数据就毫无意义. 本文对蜜网中捕获的日志模块数据利用数据挖掘技术进行标记分类,使用分类算法对已经分好类的数据进行有规则的挖掘,从而发现入侵者的攻击方法,为未来各种攻击行为做好防御准备.  相似文献   

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