AN INCREMENTAL UPDATING ALGORITHM FOR MINING ASSOCIATION RULES

In this letter, on the basis of Frequent Pattern(FP) tree, the support function to update FP-tree is introduced, then an Incremental FP (IFP) algorithm for mining association rules is proposed. IFP algorithm considers not only adding new data into the database but also reducing old data from the database. Furthermore, it can predigest five cases to three cases.The algorithm proposed in this letter can avoid generating lots of candidate items, and it is high efficient.

Discovering Association Rules with Graph Patterns in Temporal Networks

Discovering regularities between entities in temporal graphs is vital for many real-world applications(e.g.,social recommendation,emergency event detection,and cyberattack event detection).This paper proposes temporal graph association rules(TGARs)that extend traditional graph-pattern association rules in a static graph by incorporating the unique temporal information and constraints.We introduce quality measures(e.g.,support,confidence,and diversification)to characterize meaningful TGARs that are useful and diversified.In addition,the proposed support metric is an upper bound for alternative metrics,allowing us to guarantee a superset of patterns.We extend conventional confidence measures in terms of maximal occurrences of TGARs.The diversification score strikes a balance between interestingness and diversity.Although the problem is NP-hard,we develop an effective discovery algorithm for TGARs that integrates TGARs generation and TGARs selection and shows that mining TGARs is feasible over a temporal graph.We propose pruning strategies to filter TGARs that have low support or cannot make top-k as early as possible.Moreover,we design an auxiliary data structure to prune the TGARs that do not meet the constraints during the TGARs generation process to avoid conducting repeated subgraph matching for each extension in the search space.We experimentally verify the effectiveness,efficiency,and scalability of our algorithms in discovering diversified top-k TGARs from temporal graphs in real-life applications.

Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.

Improved Pattern Tree for Incremental Frequent-Pattern Mining

By analyzing the existing prefix-tree data structure, an improved pattern tree was introduced for processing new transactions. It firstly stored transactions in a lexicographic order tree and then restructured the tree by sorting each path in a frequency-descending order. While updating the improved pattern tree, there was no need to rescan the entire new database or reconstruct a new tree for incremental updating. A test was performed on synthetic dataset T10I4D100K with 100,000 transactions and 870 items. Experimental results show that the smaller the minimum support threshold, the faster the improved pattern tree achieves over CanTree for all datasets. As the minimum support threshold increased from 2% to 3.5%, the runtime decreased from 452.71 s to 186.26 s. Meanwhile, the runtime required by CanTree decreased from 1,367.03 s to 432.19 s. When the database was updated, the execution time of im- proved pattern tree consisted of construction of original improved pattern trees and reconstruction of initial tree. The experiment results showed that the runtime was saved by about 15% compared with that of CanTree. As the number of transactions increased, the runtime of improved pattern tree was about 25% shorter than that of FP-tree. The improved pattern tree also required less memory than CanTree.

Mining Software Repository for Cleaning Bugs Using Data Mining Technique

Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and complexity.However,increasing ambiguity,irrelevance,and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories.Thus,there is a need for a repository mining technique for relevant and bug-free data prediction.This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software.To predict errors in mining data,the Apriori algorithm was used to discover association rules by fixing confidence at more than 40%and support at least 30%.The pruning strategy was adopted based on evaluation measures.Next,the rules were extracted from three projects of different domains;the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values.To evaluate the proposed approach,we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects.The evaluation showed that the results of our proposal are promising.Practitioners and developers can utilize these rules for defect prediction during early software development.

频繁项集挖掘研究前沿及展望

频繁项集挖掘是数据挖掘领域的核心任务之一,其目标是发现在数据库中频繁出现的模式。这些模式对于关联规则、分类、异常检测等多个数据挖掘任务都具有重要作用。由于随着项集大小的增加,项集的组合数量呈指数级增长,导致计算复杂性急剧上升,研究人员一直在努力开发高效的算法来解决这一问题。面向频繁项集挖掘的算法、紧凑表示和前沿应用,深入探讨不同技术的的工作原理、优势和局限性,从而对这一领域的研究现状进行全面总结。最后,进一步探讨了该领域的前沿发展趋势,指出计算效率、基于约束的频繁项集挖掘、模式的可解释性以及算法在不同领域的创新应用等未来潜在研究方向。

基于Flag-Prefix-Tree的频繁模式挖掘改进算法

稀疏数据集上,条件FP-Tree无法有效压缩且频繁构造开销大,使用伪构造的问题是数据项目未经压缩和过滤导致额外的遍历代价。文章提出了一种简单而新颖的标志前缀树(Flag-Prefix-Tree)和一种新的挖掘稀疏数据集上频繁模式的算法FPT-Mine。通过Flag-Prefix-Tree中的flag,伪构造条件树可以巧妙地过滤不频繁项目。而且flag可以在挖掘过程中递归地重用,只有非常小的开销,但节省了遍历不频繁项目的大量开销。FPT-Mine以自上向下的顺序遍历Flag-Prefix-Tree,并为每个频繁模式创建一个临时根表(Root table)来伪构造条件树,这样就不需要在每个节点上维护父节点和兄弟节点的链接。此外,FPT-Mine在树上应用了合并技术,这使得FlagPrefix-Tree越来越小。研究表明,FPT-Mine在各种稀疏数据集中具有高性能和可扩展性。FPT-Mine在所有测试数据集中的性能都优于FP-growth,当最小支持度阈值降低时,算法之间的差距增大。

数字贸易对消费者行为的影响研究

本文选取2022年抽样的淘宝数据进行分析与挖掘,并基于K-means算法对买家进行聚类分析,初步筛选出疑似刷单行为的买家和卖家。在剔除这些用户后,又利用回归分析法分析卖家获得评价、信用评价体系、卖家店铺等级对销量的影响;采用LSTM算法对销量数据的时间序列进行预测;通过Apriori关联规则算法找到买家与卖家和商品之间的关联。其中,在卖家获得评价对销量的影响中,建立奖励函数来描述好评和差评的影响,结果显示奖励函数与销量呈正相关关系。在信用评价体系对销量的影响中,服务和发货对销量的影响较大。卖家店铺等级,则无明显关系。预测的销量数据虽没有较好的结果,但给出了合理的解释。关联结果显示,买家与卖家和商品之间有一定的联系,本研究仅供参考。

An efficient algorithm for mining closed itemsets

This paper presents a new efficient algorithm for mining frequent closed itemsets. It enumerates the closed set of frequent itemsets by using a novel compound frequent itemset tree that facilitates fast growth and efficient pruning of search space. It also employs a hybrid approach that adapts search strategies, representations of projected transaction subsets, and projecting methods to the characteristics of the dataset. Efficient local pruning, global subsumption checking, and fast hashing methods are detailed in this paper. The principle that balances the overheads of search space growth and pruning is also discussed. Extensive experimental evaluations on real world and artificial datasets showed that our algorithm outperforms CHARM by a factor of five and is one to three orders of magnitude more efficient than CLOSET and MAFIA.

基于数据挖掘分析含钩藤方剂的配伍及组方规律

目的:基于数据挖掘探讨含钩藤方剂的配伍及组方规律,为钩藤的规范科学使用提供依据。方法:收集含有钩藤的相关中药方剂,并统计方剂涉及中药的使用频次、性味归经等基本信息。借助VOSviewer 1.6.15软件展示含钩藤方剂的中药配伍密度;基于Apriori算法、利用IBM SPSS Modeler 18.0软件挖掘中药间配伍组合,并绘制关联规则网络。结果:共纳入方剂313首,涉及中药330味。与钩藤配伍的中药以平肝息风药、清热药、解表药为主,临床上主要治疗眩晕、惊风、癫痫等疾病。配伍密度及关联分析显示常与钩藤配伍的中药有甘草、天麻、朱砂、僵蚕、牛黄等。结论:钩藤常与平肝息风类、清热类、解表类中药配伍应用,发挥清热平肝、息风止痉等功效,临床多用于治疗眩晕、惊风、癫痫等疾病。

稳定型心绞痛的中医证候分布及用药规律研究

目的:通过数据挖掘总结稳定型心绞痛的证候分布特点及组方用药规律。方法:搜索国家知识基础设施数据库(CNKI)、中国学术期刊数据库(CSPD)、中文科技期刊数据库(CCD)、PubMed、Web of Science数据库从建库至2022年2月收录的有关中医药治疗稳定型心绞痛的相关文献,建立方药数据库,并对高频药物进行关联规则及系统聚类分析。结果:共纳入文献105篇,处方111首,涉及患者9 786例,涵盖中医证型21种,频次最高证型为气虚血瘀,共提取出7个病性证素和2个病位证素。涉及中药123味,共计使用频次1 162次,使用频次前5位的中药分别是丹参70次、川芎69次、甘草54次、黄芪53次、当归50次。前30味高频药物关联规则分析显示,支持度最高的前3位药对为川芎-当归、川芎-赤芍、川芎-红花,聚类分析得到4个药物核心组合。结论:稳定型心绞痛常见气虚血瘀、痰瘀互结、气滞血瘀证;病性证素以实性居多,常见血瘀、痰浊,虚性证素主要为气虚;用药主要以活血化瘀、理气通滞、化痰降浊、益气通脉为法,是现代中医治疗稳定型心绞痛的主导思想。

基于频繁模式树的大数据关联规则自动挖掘算法

由于传统大数据挖掘算法效率较低,导致挖掘出的规则存在大量冗余,提出基于频繁模式树的大数据关联规则自动挖掘算法。通过扫描数据库生成频繁模式树,作为算法的实现依据,并在频繁模式树上挖掘大数据频繁模式设计关联规则自动挖掘算法。结果表明:本文算法仅需5.24s就能有效挖掘出高校学生缴费数据中的关联规则,验证了该算法运行效率较高。

基于数据挖掘的耳穴贴压治疗儿童和青少年近视选穴规律研究

目的:运用关联规则和复杂系统熵聚类分析治疗儿童和青少年近视的选穴规律。方法:计算机检索中国知网(CNKI)、万方(Wan-fang)、维普(VIP)、中国生物医学文献数据库(CBM)、PubMed、Web of science、Embase等中英文数据库自建库以来有关耳穴贴压治疗儿童青少年近视的临床研究,建立耳穴处方数据库,并采用中医传承辅助平台(V2.5)进行数据分析。结果:最终纳入文献231篇,包含61310例患者,涉及耳穴51个;选穴数量多为6~8个,干预周期平均6.5周;高频耳穴为肝穴、眼穴、肾穴等;得到由肝穴、眼穴、肾穴、屏间前穴和屏间后穴交叉配伍组成的常用耳穴组合10组,获取强关联耳穴组合规则18条;提取包含3个耳穴的核心组合19组;演化出耳穴新方6个,包括心穴+脑干穴+胰胆穴+肾穴+脾穴、皮质下穴+角窝中穴+脑干穴+胃穴、屏间后穴+屏间前穴+耳背心穴+肝穴+肾穴、屏间后穴+眼穴+大肠穴+肾穴、脑干穴+新眼穴+胃穴+胰胆穴、交感穴+神门穴+新眼穴+内分泌穴+胰胆穴。结论:本研究探索出的耳穴配伍规律、核心组合及新处方,可为今后临床治疗儿童和青少年近视提供诊治新思路。

基于粗糙集的电网业务营销时序数据关联规则挖掘模型

针对挖掘出的数据关联规则存在数目少、执行时间长、置信度低问题,提出基于粗糙集的电网业务营销时序数据关联规则挖掘模型。建立电网业务营销数据四元组,通过清洗、集成、转换、消减四步预处理电网业务营销时序数据;依据粗糙集理论设定的时序数据属性约简定义,约简电网业务营销时序数据属性;通过电网业务营销数据存在的时序关系,定义关联规则,采用压缩矩阵生成时序数据的频繁项集树,得到电网业务营销时序数据关联规则,完成关联规则挖掘模型建立。实验结果证明,应用该模型后,时序数据关联规则平均数目分别高4条和10条,平均执行时间分别减少4 s和25 s,平均置信度分别高出0.2和0.22。

基于FP-Tree的最大频繁项目集挖掘及更新算法

挖掘最大频繁项目集是多种数据挖掘应用中的关键问题,之前的很多研究都是采用Apriori类的候选项目集生成-检验方法.然而,候选项目集产生的代价是很高的,尤其是在存在大量强模式和/或长模式的时候.提出了一种快速的基于频繁模式树(FP-tree)的最大频繁项目集挖掘DMFIA(discover maximum frequent itemsets algorithm)及其更新算法UMFIA(update maximum frequent itemsets algorithm).算法UMFIA将充分利用以前的挖掘结果来减少在更新的数据库中发现新的最大频繁项目集的费用.

基于特征挖掘的电网故障诊断方法

专家系统在应用方面的主要瓶颈是:规则库的维护;推理的速度和准确度的协调。分析了故障信息序列中必有或特有的信息,提出了基于特征挖掘的关联规则挖掘方法。结合电网故障信息的特征,改进了频繁模式(frequent pattern,FP)–算法:考虑了故障信息的特征,如时序和因果关联关系、故障性质、严重故障、稀有故障等因素;增加了规则的"或"逻辑;改进了FP-树的修剪技术。算例表明该算法能够大量减少无效挖掘,推理速度和准确度显著提高,适用于在线诊断。

一种基于栈变换的高效关联规则挖掘算法

在一个模式信息保全引理的基础上 ,提出了一个基于频繁模式栈变换的关联规则挖掘算法FPST ,给出了相应的栈构造和栈变换的算法描述 ,并进行了算法的性能分析和比较试验 。

基于数据挖掘的入侵检测系统框架

提出了基于分布式数据挖掘的入侵检测系统框架 .详细讨论了该系统的实现方案、模块结构和关键技术 ,最后给出了系统训练和评价方法 .该系统以基于关联规则方法的分布式数据挖掘技术为核心 ,从而实现了规则库的自动生成和更新 ,并能有效检测大规模协同攻击 .

基于组合服务执行信息的服务选取方法研究

组合服务选取问题是服务计算领域的一个研究热点问题,已往的选取方法大多基于难以准确获取的服务QoS信息,且算法思路复杂.文中提出了一种基于组合服务执行信息的服务选取方法.该方法分为3个阶段:数据生成阶段、数据挖掘阶段和服务选取阶段,分别进行组合服务执行信息的记载和相关数据集的生成、路径分支关联规则和服务执行顺序序列模式的挖掘以及基于挖掘产生的知识模式进行服务选取.文中首先给出一种可以方便记载日志的服务组合系统架构;然后提出一种基于时间加权的算法模型,以有效地进行路径分支关联规则和顺序序列模式的挖掘;最后对文中的组合服务选取方法进行描述.实验结果表明:文中方法在选取出的组合服务健壮性方面要优于基于QoS的方法.