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A related degree-based frequent pattern mining algorithm for railway fault data
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作者 Jiaxu Guo Ding Ding +2 位作者 Peihan Yang Qi Zou Yaping Huang 《High-Speed Railway》 2024年第2期101-109,共9页
It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm.However,high utility quantitative freq... It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm.However,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale up.In the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUQI-Miner),to enable the effective mining of railway fault data.The algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent patterns.Subsequently,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the algorithm.The experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies. 展开更多
关键词 High utility QUANTITATIVE frequent pattern mining Related degree pruning Fixed pattern length
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High Utility Periodic Frequent Pattern Mining in Multiple Sequences
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作者 Chien-Ming Chen Zhenzhou Zhang +1 位作者 Jimmy Ming-Tai Wu Kuruva Lakshmanna 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第10期733-759,共27页
Periodic patternmining has become a popular research subject in recent years;this approach involves the discoveryof frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pa... Periodic patternmining has become a popular research subject in recent years;this approach involves the discoveryof frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic patternmining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodicpatterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequencesis more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences isimportant. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To addressexisting problems, three new measures are defined: the utility, high support, and high-utility period sequenceratios. Further, a new upper bound, upSeqRa, and two new pruning properties were proposed. MHUPFPS usesa newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improvethe overall performance of the algorithm. Furthermore, the proposed algorithmis evaluated using several datasets.The experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utilityperiodic frequent patterns. 展开更多
关键词 Decision making frequent periodic pattern multi-sequence database sequential rules utility mining
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Association RuleMining Frequent-Pattern-Based Intrusion Detection in Network
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作者 S.Sivanantham V.Mohanraj +1 位作者 Y.Suresh J.Senthilkumar 《Computer Systems Science & Engineering》 SCIE EI 2023年第2期1617-1631,共15页
In the network security system,intrusion detection plays a significant role.The network security system detects the malicious actions in the network and also conforms the availability,integrity and confidentiality of da... In the network security system,intrusion detection plays a significant role.The network security system detects the malicious actions in the network and also conforms the availability,integrity and confidentiality of data informa-tion resources.Intrusion identification system can easily detect the false positive alerts.If large number of false positive alerts are created then it makes intrusion detection system as difficult to differentiate the false positive alerts from genuine attacks.Many research works have been done.The issues in the existing algo-rithms are more memory space and need more time to execute the transactions of records.This paper proposes a novel framework of network security Intrusion Detection System(IDS)using Modified Frequent Pattern(MFP-Tree)via K-means algorithm.The accuracy rate of Modified Frequent Pattern Tree(MFPT)-K means method infinding the various attacks are Normal 94.89%,for DoS based attack 98.34%,for User to Root(U2R)attacks got 96.73%,Remote to Local(R2L)got 95.89%and Probe attack got 92.67%and is optimal when it is compared with other existing algorithms of K-Means and APRIORI. 展开更多
关键词 IDS K-MEANS frequent pattern tree false alert mining L1-norm
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Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining
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作者 Abdirahman Alasow Marek Perkowski 《Journal of Quantum Information Science》 CAS 2023年第1期1-23,共23页
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 corre... 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. 展开更多
关键词 Data mining Association Rule mining frequent pattern Apriori Algorithm Quantum Counter Quantum Comparator Grover’s Search Algorithm
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SWFP-Miner: an efficient algorithm for mining weighted frequent pattern over data streams
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作者 Wang Jie Zeng Yu 《High Technology Letters》 EI CAS 2012年第3期289-294,共6页
Previous weighted frequent pattern (WFP) mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted freque... Previous weighted frequent pattern (WFP) mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted frequent pattern over data streams. SWFP-Miner is based on sliding window and can discover important frequent pattern from the recent data. A new refined weight definition is proposed to keep the downward closure property, and two pruning strategies are presented to prune the weighted infrequent pattern. Experimental studies are performed to evaluate the effectiveness and efficiency of SWFP-Miner. 展开更多
关键词 weighted frequent pattern (WFP) mining data streams data mining slidingwindow SWFP-Miner
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Hybrid Recommender System Using Systolic Tree for Pattern Mining
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作者 S.Rajalakshmi K.R.Santha 《Computer Systems Science & Engineering》 SCIE EI 2023年第2期1251-1262,共12页
A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking in... A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved. 展开更多
关键词 Recommender systems hybrid recommender systems frequent pattern mining collaborativefiltering systolic tree river formation dynamics particle swarm optimization
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A New Algorithm for Mining Frequent Pattern 被引量:2
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作者 李力 靳蕃 《Journal of Southwest Jiaotong University(English Edition)》 2002年第1期10-20,共11页
Mining frequent pattern in transaction database, time series databases, and many other kinds of databases have been studied popularly in data mining research. Most of the previous studies adopt Apriori like candidat... Mining frequent pattern in transaction database, time series databases, and many other kinds of databases have been studied popularly in data mining research. Most of the previous studies adopt Apriori like candidate set generation and test approach. However, candidate set generation is very costly. Han J. proposed a novel algorithm FP growth that could generate frequent pattern without candidate set. Based on the analysis of the algorithm FP growth, this paper proposes a concept of equivalent FP tree and proposes an improved algorithm, denoted as FP growth * , which is much faster in speed, and easy to realize. FP growth * adopts a modified structure of FP tree and header table, and only generates a header table in each recursive operation and projects the tree to the original FP tree. The two algorithms get the same frequent pattern set in the same transaction database, but the performance study on computer shows that the speed of the improved algorithm, FP growth * , is at least two times as fast as that of FP growth. 展开更多
关键词 data mining algorithm frequent pattern set FP growth
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A Novel Incremental Mining Algorithm of Frequent Patterns for Web Usage Mining 被引量:1
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作者 DONG Yihong ZHUANG Yueting TAI Xiaoying 《Wuhan University Journal of Natural Sciences》 CAS 2007年第5期777-782,共6页
Because data warehouse is frequently changing, incremental data leads to old knowledge which is mined formerly unavailable. In order to maintain the discovered knowledge and patterns dynamically, this study presents a... Because data warehouse is frequently changing, incremental data leads to old knowledge which is mined formerly unavailable. In order to maintain the discovered knowledge and patterns dynamically, this study presents a novel algorithm updating for global frequent patterns-IPARUC. A rapid clustering method is introduced to divide database into n parts in IPARUC firstly, where the data are similar in the same part. Then, the nodes in the tree are adjusted dynamically in inserting process by "pruning and laying back" to keep the frequency descending order so that they can be shared to approaching optimization. Finally local frequent itemsets mined from each local dataset are merged into global frequent itemsets. The results of experimental study are very encouraging. It is obvious from experiment that IPARUC is more effective and efficient than other two contrastive methods. Furthermore, there is significant application potential to a prototype of Web log Analyzer in web usage mining that can help us to discover useful knowledge effectively, even help managers making decision. 展开更多
关键词 incremental algorithm association rule frequent pattern tree web usage mining
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Mining Maximal Frequent Patterns in a Unidirectional FP-tree 被引量:1
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作者 宋晶晶 刘瑞新 +1 位作者 王艳 姜保庆 《Journal of Donghua University(English Edition)》 EI CAS 2006年第6期105-109,共5页
Because mining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model ... Because mining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model only finds out the maximal frequent patterns, which can generate all frequent patterns. FP-growth algorithm is one of the most efficient frequent-pattern mining methods published so far. However, because FP-tree and conditional FP-trees must be two-way traversable, a great deal memory is needed in process of mining. This paper proposes an efficient algorithm Unid_FP-Max for mining maximal frequent patterns based on unidirectional FP-tree. Because of generation method of unidirectional FP-tree and conditional unidirectional FP-trees, the algorithm reduces the space consumption to the fullest extent. With the development of two techniques: single path pruning and header table pruning which can cut down many conditional unidirectional FP-trees generated recursively in mining process, Unid_FP-Max further lowers the expense of time and space. 展开更多
关键词 data mining frequent pattern the maximal frequent pattern Unid _ FP-tree conditional Unid _ FP-tree.
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Novel Algorithm for Mining Frequent Patterns of Moving Objects Based on Dictionary Tree Improvement
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作者 Yi Chen Yulan Dong Dechang Pi 《国际计算机前沿大会会议论文集》 2018年第1期20-20,共1页
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Fast Discovering Frequent Patterns for Incremental XML Queries
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作者 PENGDun-lu QIUYang 《Wuhan University Journal of Natural Sciences》 EI CAS 2004年第5期638-646,共9页
It is nontrivial to maintain such discovered frequent query patterns in real XML-DBMS because the transaction database of queries may allow frequent updates and such updates may not only invalidate some existing frequ... It is nontrivial to maintain such discovered frequent query patterns in real XML-DBMS because the transaction database of queries may allow frequent updates and such updates may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. In this paper, two incremental updating algorithms, FUX-QMiner and FUXQMiner, are proposed for efficient maintenance of discovered frequent query patterns and generation the new frequent query patterns when new XMI, queries are added into the database. Experimental results from our implementation show that the proposed algorithms have good performance. Key words XML - frequent query pattern - incremental algorithm - data mining CLC number TP 311 Foudation item: Supported by the Youthful Foundation for Scientific Research of University of Shanghai for Science and TechnologyBiography: PENG Dun-lu (1974-), male, Associate professor, Ph.D, research direction: data mining, Web service and its application, peerto-peer computing. 展开更多
关键词 XML frequent query pattern incremental algorithm data mining
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Adaptive associative classification with emerging frequent patterns
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作者 Wang Xiaofeng Zhang Dapeng Shi Zhongzhi 《High Technology Letters》 EI CAS 2012年第1期38-44,共7页
In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine(SVM... In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine(SVM) based method to refine the discovered emerging ~equent patterns for classification rule extension for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively. 展开更多
关键词 associative classification RULE frequent pattern mining emerging frequent pattern supportvector machine (SVM)
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Mining Cross-Transaction Web Usage Patterns
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作者 Jian Chen Jian Yin Jin Huang Liangyi Ou 《通讯和计算机(中英文版)》 2005年第5期6-11,81,共7页
关键词 WEB系统 存贮器 交叉处理器 计算机技术
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一种基于关联程度的高效用数量比频繁模式挖掘算法
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作者 王辉 李燕 +2 位作者 丁丁 吴坤 黄雅平 《计算机工程与科学》 CSCD 北大核心 2024年第9期1702-1710,共9页
高效用频繁模式挖掘算法运用数据项的重要度信息,能够从数据中挖掘出更重要的频繁模式,而高效用数量比频繁模式挖掘算法可以进一步研究频繁模式中数据项的数量比例关系,是目前数据挖掘领域中的研究课题。从提高算法性能和实用性的角度... 高效用频繁模式挖掘算法运用数据项的重要度信息,能够从数据中挖掘出更重要的频繁模式,而高效用数量比频繁模式挖掘算法可以进一步研究频繁模式中数据项的数量比例关系,是目前数据挖掘领域中的研究课题。从提高算法性能和实用性的角度出发对高效用数量比频繁模式挖掘算法进行优化,提出了一种基于关联程度的高效用数量比频繁模式挖掘算法RHUQI-Miner。RHUQI-Miner首先提出关联程度的概念,依据关联程度构建项目关联程度结构,并给出关联剪枝优化策略,寻找关联程度更高的项目集合,减少冗余和无效的频繁模式;随后运用修正模式长度策略,修正挖掘过程中项集的效用信息,使算法可根据实际数据情况控制输出频繁模式的长度,进一步提升算法的性能,提高算法的实用性。通过对RHUQI-Miner在动车组PHM系统车载故障数据集上的实验结果进行分析,表明该算法能够有效减少挖掘过程中的时间以及内存消耗,可以得出该算法适用于铁路实际数据和业务的有效结论。 展开更多
关键词 高效用 数量比 频繁模式挖掘 关联剪枝 修正模式长度
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基于滑动窗口含负项的高效用模式挖掘
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作者 武妍 荀亚玲 马煜 《计算机工程与设计》 北大核心 2024年第3期845-851,共7页
针对传统高效用模式挖掘均未考虑项的效用值为负,以及对流数据处理的时效性问题,提出一种基于滑动窗口的高效用挖掘算法HUPN_SW。利用一种新定义的滑动窗口正负效用列表PNSWU-List,维护挖掘最近批次高效用模式集所需的所有信息,实现有... 针对传统高效用模式挖掘均未考虑项的效用值为负,以及对流数据处理的时效性问题,提出一种基于滑动窗口的高效用挖掘算法HUPN_SW。利用一种新定义的滑动窗口正负效用列表PNSWU-List,维护挖掘最近批次高效用模式集所需的所有信息,实现有效的逐批次挖掘,避免重复的数据库扫描,在不产生候选效用模式集的情况下,直接挖掘出高效用模式,使HUPN_SW有效适应于动态流数据。实验结果表明,HUPN_SW算法在运行时间和可扩展性方面有良好表现。 展开更多
关键词 频繁模式挖掘 滑动窗口 高效用模式挖掘 高效用项集 负效用 流数据 效用列表
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Fast FP-Growth for association rule mining 被引量:1
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作者 杨明 杨萍 +1 位作者 吉根林 孙志挥 《Journal of Southeast University(English Edition)》 EI CAS 2003年第4期320-323,共4页
In this paper, we propose an efficient algorithm, called FFP-Growth (shortfor fast FP-Growth) , to mine frequent itemsets. Similar to FP-Growth, FFP-Growth searches theFP-tree in the bottom-up order, but need not cons... In this paper, we propose an efficient algorithm, called FFP-Growth (shortfor fast FP-Growth) , to mine frequent itemsets. Similar to FP-Growth, FFP-Growth searches theFP-tree in the bottom-up order, but need not construct conditional pattern bases and sub-FP-trees,thus, saving a substantial amount of time and space, and the FP-tree created by it is much smallerthan that created by TD-FP-Growth, hence improving efficiency. At the same time, FFP-Growth can beeasily extended for reducing the search space as TD-FP-Growth (M) and TD-FP-Growth (C). Experimentalresults show that the algorithm of this paper is effective and efficient. 展开更多
关键词 data mining frequent itemsets association rules frequent pattern tree(FP-tree)
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SFPMax——基于排序FP树的最大频繁模式挖掘算法 被引量:26
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作者 秦亮曦 史忠植 《计算机研究与发展》 EI CSCD 北大核心 2005年第2期217-223,共7页
FP-growth算法是目前较高效的频繁模式挖掘算法之一 ,但将它用于最大频繁模式挖掘时却不能获得较高的效率 深入分析了造成低效的原因 ,提出了利用排序FP 树挖掘最大频繁模式的算法SFP- Max 算法的主要思想如下 :①基于排序FP 树 ;②利... FP-growth算法是目前较高效的频繁模式挖掘算法之一 ,但将它用于最大频繁模式挖掘时却不能获得较高的效率 深入分析了造成低效的原因 ,提出了利用排序FP 树挖掘最大频繁模式的算法SFP- Max 算法的主要思想如下 :①基于排序FP 树 ;②利用最大频繁模式的性质 ,减小产生的候选最大模式的规模 ;③设置中间结果集 ,缩小检验的范围 ,从而减少检验候选最大模式的时间 实验表明 ,SFP -Max是一个高效的最大频繁模式的挖掘算法 ,对于测试的数据集 ,SFP 展开更多
关键词 数据挖掘 关联规则 排序FP-树 最大频繁模式
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基于时效性和相关性约束的周期模式挖掘
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作者 闫海博 荀亚玲 +2 位作者 任姿芊 侯亚飞 胡晓莹 《计算机应用研究》 CSCD 北大核心 2024年第4期1064-1069,共6页
传统周期模式挖掘忽略了模式本身的相关性和时效性,导致获取到一些实用价值有限的弱相关且时效性较低的模式。因此,提出了新颖的基于时效性和相关性约束的周期模式挖掘方法(correlation and recency periodic frequent pattern-breadth ... 传统周期模式挖掘忽略了模式本身的相关性和时效性,导致获取到一些实用价值有限的弱相关且时效性较低的模式。因此,提出了新颖的基于时效性和相关性约束的周期模式挖掘方法(correlation and recency periodic frequent pattern-breadth first search,CRPFP-BFS)和(correlation and recency periodic frequent pattern-depth first search,CRPFP-DFS)。将给定的数据库压缩到一个列式结构的列表CRPFP-List中,CRPFP-BFS和CRPFP-DFS分别采用广度优先和深度优先搜索方式递归地进行挖掘,同时利用支持度、周期、时效性以及相关性剪枝策略减少搜索空间,以有效地发现相关时效周期模式。与当前最先进算法在密集数据集和稀疏数据集上进行对比实验,结果表明CRPFP-BFS和CRPFP-DFS具有较低的内存占用和更高的运行效率,并且具有良好的可扩展性,其中CRPFP-DFS适合于内存要求严格的情况,CRPFP-BFS在长事务稀疏数据集下的运行效率更高。 展开更多
关键词 频繁模式挖掘 周期模式 相关时效周期模式 相关性 时效性
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面向大图的Top-Rank-K频繁模式挖掘算法
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作者 邹杰军 王欣 +5 位作者 石俊豪 兰卓 方宇 张翀 谢文波 沈玲珍 《南京大学学报(自然科学版)》 CAS CSCD 北大核心 2024年第1期38-52,共15页
频繁模式挖掘(Frequent Pattern Mining,FPM)在社交分析中扮演重要角色,能从海量社交数据中挖掘用户行为的模式和规律,为社交网络的研究提供新的认识和决策支持.然而,对于一个FPM任务,设置一个合适的支持度阈值不容易;此外,FPM作为一个N... 频繁模式挖掘(Frequent Pattern Mining,FPM)在社交分析中扮演重要角色,能从海量社交数据中挖掘用户行为的模式和规律,为社交网络的研究提供新的认识和决策支持.然而,对于一个FPM任务,设置一个合适的支持度阈值不容易;此外,FPM作为一个NP-hard问题,不存在多项式时间的算法.针对上述问题,提出一种无须用户设置初始支持度阈值的算法ItrMiner.该算法使用一种新的兴趣度指标对模式进行评估,综合考虑模式的大小和支持度,挖掘Top-Rank-K频繁模式.同时,为了解决去除初始支持度阈值后在算法剪枝阶段遇到的困难,提出基于树模式优先识别的策略和模式扩展约束策略,减少非必要候选模式的生成.在真实图和人工合成图数据集上进行了广泛的实验,证明ItrMiner在执行效率和可扩展性方面表现出色,尤其在稠密的数据集上,其时间开销仅为基线算法Top-K Graph Miner的13.2%.另外,提出的模式扩展约束策略的有效性较高,和无扩展约束优化的ItrMiner_(nopt)算法相比,效率提升最高可达9.5倍. 展开更多
关键词 频繁模式挖掘 社交分析 支持度阈值 兴趣度
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频繁时序模式挖掘方法综述
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作者 唐增金 徐贞顺 +3 位作者 苏梦瑶 刘纳 王振彪 张文豪 《计算机工程与应用》 CSCD 北大核心 2024年第17期48-61,共14页
频繁时序模式挖掘是指从时间序列数据中发现频繁出现的模式或规律的过程,其目的是可以帮助理解时间序列数据中的重要特征,例如周期性、趋势和异常等,有助于预测未来的发展趋势和识别异常情况等。根据近年来的频繁时序模式挖掘方法的相... 频繁时序模式挖掘是指从时间序列数据中发现频繁出现的模式或规律的过程,其目的是可以帮助理解时间序列数据中的重要特征,例如周期性、趋势和异常等,有助于预测未来的发展趋势和识别异常情况等。根据近年来的频繁时序模式挖掘方法的相关文献调研,按照关键技术和代表性算法将其分为三类,即基于结构约束的频繁时序模式挖掘方法、基于参数约束的频繁时序模式挖掘方法和基于窗口的频繁时序模式挖掘方法。陈述了频繁时序模式挖掘方法的背景以及各方法的特点;分别介绍了三类挖掘方法的发展以及分类,并从优缺点和性能等方面对各类改进方法进行了详细的对比分析;对频繁时序模式挖掘方法进行归纳和总结,并对频繁时序模式挖掘方法的未来研究方向进行了展望。 展开更多
关键词 时序数据 频繁时序模式 结构约束 参数约束 窗口 数据挖掘
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