一种矩阵和排序索引关联规则数据挖掘算法

在关联规则挖掘算法中,Apriori由于多次对数据库进行扫描会产生较多的候选集,在多次扫描数据库的情况下容易产生I/O开销问题,并引起数据挖掘效率低.矩阵关联规则在数据挖掘过程中没有删除非频繁项集,致使存在较多的无效扫描,对于挖掘效率的提高也不明显.该文提出了一种改进的矩阵和排序索引关联规则数据挖掘算法,首先,删除不需...     

A cost-efficient and versatile sanitizing algorithm by using a greedy approach

In a very large database, there exists sensitive information that must be protected against unauthorized accesses. The confidentiality protection of the information has been a long-term goal pursued by the database security research community and the government statistical agencies. In this paper, we proposed greedy methods for hiding sensitive rules. The experimental results showed the effectiveness of our approaches in terms of undesired side effects avoided in the rule hiding process. The results also revealed that in most cases, all the sensitive rules are hidden without generating spurious rules. First, the good scalability of our approach in terms of database sizes was achieved by using an efficient data structure, FCET, to store only maximal frequent itemsets instead of storing all frequent itemsets. Furthermore, we also proposed a new framework for enforcing the privacy in mining association rules. In the framework, we combined the techniques of efficiently hiding sensitive rules with the transaction retrieval engine based on the FCET index tree. For hiding sensitive rules, the proposed greedy approach includes a greedy approximation algorithm and a greedy exhausted algorithm to sanitize the database. In particular, we presented four strategies in the sanitizing procedure and four strategies in the exposed procedure, respectively, for hiding a group of association rules characterized as sensitive or artificial rules. In addition, the exposed procedure would expose missing rules during the processing so that the number of missing rules could be lowered as much as possible.     

基于影响度的隐私保护关联规则挖掘算法

将T检验思想引入隐私保护数据挖掘算法,提出基于影响度的隐私保护关联规则挖掘算法.将影响度作为关联规则生成准则,以减少冗余规则和不相关规则,提高挖掘效率;通过调整事务间敏感关联规则的项目,实现敏感规则隐藏.实验结果表明,该算法能使规则损失率和增加率降低到6%以下.     

一种集成数据挖掘的自动视频分类方法

针对自动视频分类工作中分类预测精度低的问题，提出了一种集成数据挖掘技术的自动视频分类方法。首先进行视频分割，形成了一个视频属性数据库；然后分别使用决策树、分类关联规则等技术对视频属性数据库进行数据挖掘，提取出决策树分类规则集和分类关联规则集；最后利用一个规则集的合并裁减算法来合并这两个分类预测规则集，形成最终的具有更高精度的视频分类规则集。通过实验验证了决策树分类预测规则和分类关联规则具有分类预测的一致性；同时实验表明，使用合并后的规则集比单独使用一个规则集来预测视频具有更高的预测准确率。     

PARM—An Efficient Algorithm to Mine Association Rules From Spatial Data

Association rule mining, originally proposed for market basket data, has potential applications in many areas. Spatial data, such as remote sensed imagery (RSI) data, is one of the promising application areas. Extracting interesting patterns and rules from spatial data sets, composed of images and associated ground data, can be of importance in precision agriculture, resource discovery, and other areas. However, in most cases, the sizes of the spatial data sets are too large to be mined in a reasonable amount of time using existing algorithms. In this paper, we propose an efficient approach to derive association rules from spatial data using Peano Count Tree (P-tree) structure. P-tree structure provides a lossless and compressed representation of spatial data. Based on P-trees, an efficient association rule mining algorithm PARM with fast support calculation and significant pruning techniques is introduced to improve the efficiency of the rule mining process. The P-tree based Association Rule Mining (PARM) algorithm is implemented and compared with FP-growth and Apriori algorithms. Experimental results showed that our algorithm is superior for association rule mining on RSI spatial data.      

一种基于倒排索引树的增量更新关联挖掘算法

增量更新关联规则挖掘主要解决事务数据库中交易记录不断更新和最小支持度发生变化时关联规则的维护问题。针对目前诸多增量更新关联规则挖掘算法存在效率低、计算成本高、规则难以维护等问题,提出一种基于倒排索引树的增量更新关联挖掘算法。该算法有效地将倒排索引技术与树型结构相结合,使得交易数据库中的数据不断更新和最小支持度随应用环境不同而不断改变时,以实现无需扫描原始交易数据库和不产生候选项集的情况下生成频繁项集。实验结果表明,该算法只需占用较小的存储空间、且检索项集的效率较高,能高效地解决增量更新关联规则难以维护的问题。     

基于存储改进的分区并行关联规则挖掘算法

针对现有算法存储结构简单、生成大量冗余的候选集、时间和空间复杂度高,挖掘效率不理想的情况,为了进一步提高关联规则算法挖掘频繁集的速度,优化算法的执行性能,提出基于内存结构改进的关联规则挖掘算法。该算法基于Spark分布式框架,分区并行挖掘出频繁集,提出在挖掘过程中利用布隆过滤器进行项目存储,并对事务集和候选集进行精简化操作,进而达到优化挖掘频繁集的速度、节省计算资源的目的。算法在占用较少内存的条件下,相比于YAFIM和MR-Apriori算法,在挖掘频繁集效率上有明显的提升,不但能较好地提升挖掘速度,降低内存的压力,而且具有很好的可扩展性,使得算法可以应用到更大规模的数据集和集群,从而达到优化算法性能的目的。     

关联规则隐藏算法的研究

数据挖掘能从不同角度、不同抽象层上看待数据,这将潜在地影响数据的私有性和安全性。着重介绍了关联规则数据挖掘中的规则隐藏算法,提出了一个改进的关联规则隐藏算法OSA,该算法综合采用项的添加和约束方法来降低关联规则的支持度和置信度,从而达到规则隐藏的目的。     

医疗健康数据的模糊粗糙集规则挖掘方法研究

医疗健康数据通常属性较多,且存在连续型、离散型并存的混合数据,这在很大程度上限制了知识发现方法对医疗健康数据的挖掘效率。以模糊粗糙集理论为基础,研究混合数据上的分类规则挖掘方法,通过引入规则获取算法的泛化阈值,来控制获取规则集的大小和复杂程度,提高粗糙集知识发现方法在医疗健康数据上的分类效率。最后通过对比实验验证了该算法在医疗决策表上挖掘规则的有效性。     

基于垂直数据分布的大型稠密数据库快速关联规则挖掘算法

为进一步解决对大型事务数据库进行关联规则挖掘时产生的CPU时间开销大和I/O操作频繁的问题,给出了一种基于垂直数据分布的改进关联规则挖掘算法,称为VARMLDb算法。该算法首先有效地把数据库分为内存可以满足要求的若干划分,然后结合有向无环图和垂直数据形式diffse、差集来存储和计算频繁项集,极大地减少了存储中间结果所需的内存大小,解决了传统垂直数据挖掘算法对稠密数据库挖掘效率低下的问题,使该算法可有效地适用于大型稠密数据库的关联规则挖掘。整个算法吸取CARMA算法的优势,只需扫描两次数据库便可完成挖掘过程。实验结果表明该算法是正确的,在大型稠密数据库中,VARMLDb算法具有较高的执行效率。     

Positive and negative association rule mining on XML data streams in database as a service concept

In recent years, data mining has become one of the most popular techniques for data owners to determine their strategies. Association rule mining is a data mining approach that is used widely in traditional databases and usually to find the positive association rules. However, there are some other challenging rule mining topics like data stream mining and negative association rule mining. Besides, organizations want to concentrate on their own business and outsource the rest of their work. This approach is named “database as a service concept” and provides lots of benefits to data owner, but, at the same time, brings out some security problems. In this paper, a rule mining system has been proposed that provides efficient and secure solution to positive and negative association rule computation on XML data streams in database as a service concept. The system is implemented and several experiments have been done with different synthetic data sets to show the performance and efficiency of the proposed system.     

基于矩阵的关联规则挖掘算法研究与改进

为了有效提高关联规则挖掘算法处理数据库的效率,在研究基于矩阵的关联规则挖掘算法的基础上,提出了改进的关联规则挖掘算法DMApriori,并选取程序模拟超市购物产生的4个试验数据集,应用DMapriori算法对该数据集进行了关联规则挖掘;实验结果表明,该算法能平均提高关联规则挖掘时间20%;在计算数据库中的频繁项集时,通过有效裁剪布尔矩阵,使算法逐层扫描的数据量大大减少,并且对每个项集计数时,只扫描部分数据,提高了关联规则挖掘算法的性能。     

High performance evaluation of evolutionary-mined association rules on GPUs

Association rule mining is a well-known data mining task, but it requires much computational time and memory when mining large scale data sets of high dimensionality. This is mainly due to the evaluation process, where the antecedent and consequent in each rule mined are evaluated for each record. This paper presents a novel methodology for evaluating association rules on graphics processing units (GPUs). The evaluation model may be applied to any association rule mining algorithm. The use of GPUs and the compute unified device architecture (CUDA) programming model enables the rules mined to be evaluated in a massively parallel way, thus reducing the computational time required. This proposal takes advantage of concurrent kernels execution and asynchronous data transfers, which improves the efficiency of the model. In an experimental study, we evaluate interpreter performance and compare the execution time of the proposed model with regard to single-threaded, multi-threaded, and graphics processing unit implementation. The results obtained show an interpreter performance above 67 billion giga operations per second, and speed-up by a factor of up to 454 over the single-threaded CPU model, when using two NVIDIA 480 GTX GPUs. The evaluation model demonstrates its efficiency and scalability according to the problem complexity, number of instances, rules, and GPU devices.     

Application of particle swarm optimization to association rule mining

In the area of association rule mining, most previous research had focused on improving computational efficiency. However, determination of the threshold values of support and confidence, which seriously affect the quality of association rule mining, is still under investigation. Thus, this study intends to propose a novel algorithm for association rule mining in order to improve computational efficiency as well as to automatically determine suitable threshold values. The particle swarm optimization algorithm first searches for the optimum fitness value of each particle and then finds corresponding support and confidence as minimal threshold values after the data are transformed into binary values. The proposed method is verified by applying the FoodMart2000 database of Microsoft SQL Server 2000 and compared with a genetic algorithm. The results indicate that the particle swarm optimization algorithm really can suggest suitable threshold values and obtain quality rules. In addition, a real-world stock market database is employed to mine association rules to measure investment behavior and stock category purchasing. The computational results are also very promising.     

模糊关联规则算法在入侵检测中的应用

提出了一种结合Apriori和Kuok's算法的改进的模糊关联规则算法.在定义隶属函数、决策树结构和规则集相似度的基础上,采用改进的挖掘算法挖掘数值属性的关联规则.实验结果表明,算法在规则生成和时间效率方面都显示了良好的性能.     

Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining

Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets; for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms (GA); the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms; 2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.’s work in terms of runtime, number of large itemsets and number of association rules.     

一种事务互补挖掘算法的研究及应用

提出一种事务互补的挖掘算法,其适合挖掘任何长度的频繁项目集。该算法用事务互补搜索策略产生候选项,使用频繁项目集修剪其子集和非频繁项目集修剪其超集策略减少候选项;在计算支持数时使用了二进制的逻辑运算和事务特性,提高了算法的效率。将其应用到横向空间关联规则挖掘中,实验表明该算法是快速而有效的。     

SparkSql上自适应数据集的高效频繁集挖掘算法

针对基于Spark框架的关联规则算法存在I/O开销大、数据结构和挖掘频繁集方式单一、计算支持度的方式效率低等问题,提出基于SparkSql进行分布式编程的算法。将数据集加载到DataFrame,利用改进后的布隆过滤器高效存储频繁集挖掘过程中产生的项集,解决RDD内存资源和计算速度受限问题。基于先验定理对事务、项目和项集进行精简,同时提出用Sql语句对项集中项目对应事务集合求交集的方式计算项集支持度,提高计算支持度的效率。提出了两种迭代算法和自适应数据的选择条件,增强该算法对各种数据集的泛化性。进行多组实验,证明提出的算法总是自适应本次迭代数据的特点选择最优的迭代方法,同时具有较高并行算法性能,可以扩展到更大规模集群和数据;同基于Spark框架的关联规则算法YAFIM和R-Apriori进行对比,在每次迭代和总体运行计算效率上有更好的表现。     

关联规则挖掘中Apriori改进算法的研究

挖掘频繁项集是许多数据挖掘任务中的关键问题,也是关联规则挖掘算法的核心,提高频繁项集的生成效率一直是近几年数据挖掘领域研究的热点之一.在对关联规则挖 掘中基于Apriori算法的改进算法进行深入分析和研究后,本文根据Apriori算法的不足,提出了一种改进策略,从而得到一种优化的Apriori算法.最后,对频繁项集挖掘算法的发展方向进行了初步的探讨.     

A greedy classification algorithm based on association rule

Classification and association rule discovery are important data mining tasks. Using association rule discovery to construct classification systems, also known as associative classification, is a promising approach. In this paper, a new associative classification technique, Ranked Multilabel Rule (RMR) algorithm is introduced, which generates rules with multiple labels. Rules derived by current associative classification algorithms overlap in their training objects, resulting in many redundant and useless rules. However, the proposed algorithm resolves the overlapping between rules in the classifier by generating rules that does not share training objects during the training phase, resulting in a more accurate classifier. Results obtained from experimenting on 20 binary, multi-class and multi-label data sets show that the proposed technique is able to produce classifiers that contain rules associated with multiple classes. Furthermore, the results reveal that removing overlapping of training objects between the derived rules produces highly competitive classifiers if compared with those extracted by decision trees and other associative classification techniques, with respect to error rate.