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GTK:A Hybrid-Search Algorithm of Top-Rank-k Frequent Patterns Based on Greedy Strategy 认领 引用 被引量：1
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作者 Yuhang Long Wensheng Tang +4 位作者 Bo Yang Xinyu Wang Hua Ma Hang Shi Xueyu Cheng 《Computers, Materials & Continua》 SCIE EI 2020年第6期1445-1469,共25页
Currently,the top-rank-k has been widely applied to mine frequent patterns with a rank not exceeding k.In the existing algorithms,although a level-wise-search could fully mine the target patterns,it usually leads to t... Currently,the top-rank-k has been widely applied to mine frequent patterns with a rank not exceeding k.In the existing algorithms,although a level-wise-search could fully mine the target patterns,it usually leads to the delay of high rank patterns generation,resulting in the slow growth of the support threshold and the mining efficiency.Aiming at this problem,a greedy-strategy-based top-rank-k frequent patterns hybrid mining algorithm(GTK)is proposed in this paper.In this algorithm,top-rank-k patterns are stored in a static doubly linked list called RSL,and the patterns are divided into short patterns and long patterns.The short patterns generated by a rank-first-search always joins the two patterns of the highest rank in RSL that have not yet been joined.On the basis of the short patterns satisfying specific conditions,the long patterns are extracted through level-wise-search.To reduce redundancy,GTK improves the generation method of subsume index and designs the new pruning strategies of candidates.This algorithm also takes the use of reasonable pruning strategies to reduce the amount of computation to improve the computational speed.Real datasets and synthetic datasets are adopted in experiments to evaluate the proposed algorithm.The experimental results show the obvious advantages in both time efficiency and space efficiency of GTK. 展开更多
关键词 Top-rank-k frequent patterns greedy strategy hybrid-search
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Mining Maximal Frequent Patterns in a Unidirectional FP-tree 认领 引用 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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A Novel Incremental Mining Algorithm of Frequent Patterns for Web Usage Mining 认领 引用 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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Adaptive associative classification with emerging frequent patterns 认领 引用
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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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Fast Discovering Frequent Patterns for Incremental XML Queries 认领 引用
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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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Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining 认领 引用 被引量：1
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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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Novel Algorithm for Mining Frequent Patterns of Moving Objects Based on Dictionary Tree Improvement 认领 引用
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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》 EI 2018年第1期20-20,共1页
Frequent pattern mining is one of the hotspots in the research of moving object data mining. In recent years, with the rapid development of various wireless communication technologies, such as Bluetooth, Wi-Fi, GPRS, ... Frequent pattern mining is one of the hotspots in the research of moving object data mining. In recent years, with the rapid development of various wireless communication technologies, such as Bluetooth, Wi-Fi, GPRS, 3G, and so on, more and more mobile devices have been used in various applications. In the face of so many data, the following problem is how to deal with and apply the massive data stored in the database, and the theory and technology of data mining emerge as the times require. Therefore, frequent pattern mining for moving objects is very necessary, meaningful and valuable. This paper draws on the existing algorithms for mining frequent patterns of moving objects, and puts forward some innovations. Aiming at the defect of low time efficiency for common frequent mode Apriori algorithm, an improved algorithm named IAA-DT based on dictionary tree is proposed in this paper. The algorithm first traverses all the trajectories in the database and adds it to the dictionary tree. At the same time, it also needs to maintain the linked list to facilitate the pruning of invalid entries. Because of the high degree of com- pressibility of the dictionary tree, it can be counted directly on the dictionary tree, which greatly improves the time efficiency. The IAA-DT algorithm and the existing improved Apriori-like algorithm based on SQL are compared and analyzed by using the open real data of taxi trajectories in San Francisco. The experimental results with different minimum support levels demonstrate the effectiveness and efficiency of our IAA-DT method. 展开更多
关键词 Frequent pattern Moving trajectory Apriori algorithmData mining
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An Efficient Hybrid Algorithm for Mining Web Frequent Access Patterns 认领 引用 被引量：1
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作者 ZHANLi-qiang LIUDa-xin 《Wuhan University Journal of Natural Sciences》 EI CAS 2004年第5期557-560,共4页
We propose an efficient hybrid algorithm WDHP in this paper for mining frequent access patterns. WDHP adopts the techniques of DHP to optimize its performance, which is using hash table to filter candidate set and tri... We propose an efficient hybrid algorithm WDHP in this paper for mining frequent access patterns. WDHP adopts the techniques of DHP to optimize its performance, which is using hash table to filter candidate set and trimming database. Whenever the database is trimmed to a size less than a specified threshold, the algorithm puts the database into main memory by constructing a tree, and finds frequent patterns on the tree. The experiment shows that WDHP outperform algorithm DHP and main memory based algorithm WAP in execution efficiency. 展开更多
关键词 frequent access pattern AP-tree hash-table
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Frequent Pattern Growth-Based Identification of Critical Lines in Cascading Failures for Renewable-Dominant Hybrid AC/DC Power Systems 认领 引用 被引量：1
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作者 Tianhao Liu Jiongcheng Yan Yutian Liu 《Engineering》 SCIE EI CSCD 2025年第8期158-170,共13页
In wind and solar renewable-dominant hybrid alternating current/direct current(AC/DC)power systems,the active power of high-voltage direct current(HVDC)system is significantly limited by the security and stability eve... In wind and solar renewable-dominant hybrid alternating current/direct current(AC/DC)power systems,the active power of high-voltage direct current(HVDC)system is significantly limited by the security and stability events caused by cascading failures.To identify critical lines in cascading failures,a rapid risk assessment method is proposed based on the gradient boosting decision tree(GBDT)and frequent pat-tern growth(FP-Growth)algorithms.First,security and stability events triggered by cascading failures are analyzed to explain the impact of cascading failures on the maximum DC power.Then,a cascading failure risk index is defined,focusing on the DC power being limited.To handle the strong nonlinear relationship between the maximum DC power and cascading failures,a GBDT with an update strategy is utilized to rapidly predict the maximum DC power under uncertain operating conditions.Finally,the FP-Growth algorithm is improved to mine frequent patterns in cascading failures.The importance index for each fault in a frequent pattern is defined by evaluating its impact on cascading failures,enabling the identification of critical lines.Simulation results of a modified Ningxia–Shandong hybrid AC/DC system in China demonstrate that the proposed method can rapidly assess the risk of cascading failures and effectively identify critical lines. 展开更多
关键词 Cascading failure Risk assessment Frequent pattern Hybrid AC/DC power system Renewable energy
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Research on Automatic Mining Method of Behavior Rule Based on Apriori Algorithm 认领 引用
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作者 ZHU Aiqun LU Jin 《US-China Education Review(A)》 2026年第5期295-302,共8页
Behaviour rule mining extracts valuable patterns from large amounts of behavioural data,which is crucial for analysing user behaviour,monitoring systems,and detecting security threats.Traditional manual or statistical... Behaviour rule mining extracts valuable patterns from large amounts of behavioural data,which is crucial for analysing user behaviour,monitoring systems,and detecting security threats.Traditional manual or statistical methods often fail to reveal complex,hidden associations.This study therefore proposes an automated behaviour rule mining method based on an improved Apriori algorithm.This method adapts data preprocessing and feature encoding to behavioural characteristics,introduces an adaptive support threshold and incremental updating to enhance efficiency,and automates the generation and filtering of association rules from frequent behavioural sequences.When evaluated using accuracy,recall,and interpretability metrics on public user behaviour data and simulated system logs,the method was found to effectively mine meaningful rules while maintaining high efficiency with large-scale data.This work offers a scalable and interpretable approach to automated behaviour rule mining that supports intelligent analysis and decision-making. 展开更多
关键词 Apriori algorithm behavior mining frequent patterns association rules automated analysis data mining
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High Utility Periodic Frequent Pattern Mining in Multiple Sequences 认领 引用 被引量：1
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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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New algorithm of mining frequent closed itemsets 认领 引用 被引量：1
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作者 张亮 任永功 付玉 《Journal of Southeast University(English Edition)》 EI CAS 2008年第3期335-338,共4页
A new algorithm based on an FC-tree （frequent closed pattern tree） and a max-FCIA （maximal frequent closed itemsets algorithm） is presented, which is used to mine the frequent closed itemsets for solving memory an... A new algorithm based on an FC-tree （frequent closed pattern tree） and a max-FCIA （maximal frequent closed itemsets algorithm） is presented, which is used to mine the frequent closed itemsets for solving memory and time consuming problems. This algorithm maps the transaction database by using a Hash table,gets the support of all frequent itemsets through operating the Hash table and forms a lexicographic subset tree including the frequent itemsets.Efficient pruning methods are used to get the FC-tree including all the minimum frequent closed itemsets through processing the lexicographic subset tree.Finally,frequent closed itemsets are generated from minimum frequent closed itemsets.The experimental results show that the mapping transaction database is introduced in the algorithm to reduce time consumption and to improve the efficiency of the program.Furthermore,the effective pruning strategy restrains the number of candidates,which saves space.The results show that the algorithm is effective. 展开更多
关键词 frequent itemsets frequent closed itemsets minimum frequent closed itemsets maximal frequent closed itemsets frequent closed pattern tree
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SWFP-Miner: an efficient algorithm for mining weighted frequent pattern over data streams 认领 引用
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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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Association RuleMining Frequent-Pattern-Based Intrusion Detection in Network 认领 引用 被引量：1
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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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Hybrid Reliability Parameter Selection Method Based on Text Mining, Frequent Pattern Growth Algorithm and Fuzzy Bayesian Network 认领 引用 被引量：1
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作者 SHUAI Yon SONG Tailian +1 位作者 WANG Jianping ZHAN Wenbin 《Journal of Shanghai Jiaotong university(Science)》 EI 2018年第3期423-428,共6页
Reliability parameter selection is very important in the period of equipment project design and demonstration. In this paper, the problem in selecting the reliability parameters and their number is proposed. In order ... Reliability parameter selection is very important in the period of equipment project design and demonstration. In this paper, the problem in selecting the reliability parameters and their number is proposed. In order to solve this problem, the thought of text mining is used to extract the feature and curtail feature sets from text data firstly, and frequent pattern tree （FPT） of the text data is constructed to reason frequent item-set between the key factors by frequent patter growth （FPC） algorithm. Then on the basis of fuzzy Bayesian network （FBN） and sample distribution, this paper fuzzifies the key attributes, which forms associated relationship in frequent item-sets and their main parameters, eliminates the subjective influence factors and obtains condition mutual information and maximum weight directed tree among all the attribute variables. Furthermore, the hybrid model is established by reason fuzzy prior probability and contingent probability and concluding parameter learning method. Finally, the example indicates the model is believable and effective. 展开更多
关键词 reliability parameter text mining frequent pattern growth(FPG) fuzzy Bayesian network(FBN)
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A related degree-based frequent pattern mining algorithm for railway fault data 认领 引用
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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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A New Algorithm for Mining Frequent Pattern 认领 引用 被引量：4
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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 candidate ... 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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Correlation analysis between characteristics under gastroscope and image information of tongue in patients with chronic gastritis 认领 引用 被引量：8
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作者 SHANG Zimeng DU Zhengguang +4 位作者 GUAN Bei JI Xingyu CHEN Longchang WANG Yongji MA Yun 《Journal of Traditional Chinese Medicine》 SCIE CSCD 2022年第1期102-107,共6页
OBJECTIVE:To explore the correlation between diagnostic information of tongue and gastroscopy results of patients with chronic gastritis.METHODS:Frequent pattern growth(FP-Growth),SPSS Modeler was used to analyze the ... OBJECTIVE:To explore the correlation between diagnostic information of tongue and gastroscopy results of patients with chronic gastritis.METHODS:Frequent pattern growth(FP-Growth),SPSS Modeler was used to analyze the correlation rules between the image information of tongue parameters and the characteristics of the stomach and duodenum seen under gastroscopy.RESULTS:Ranking in order of confidence:cyanotic tongue,slippery fur,yellow fur and spotted tongue were sequently associated with both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.L,one value of tongue coating color,which counted among(30,60),tooth-marked tongue and b,one value of tongue coating color,which counted in the range of(5,20)were sequently associated with gastric antrum mucosal erythema/macula.A,one value of tongue body color,which counted in the range of(0,20),was related to both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.a,one value of tongue coating color,which counted in the range of(15,35),was associated with gastric antrum mucosal erythema/macula.There are a total of 9 strong correlation rules.CONCLUSIONS:Cyanotic tongue,slippery fur,yellow fur,the CIE Lab value of tongue coating,a,the value of tongue body color,spotted tongue,and tooth-marked tongue are all related to the gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.The conditions of gastric mucosa could be predicted by the examination of the above related image information of tongue. 展开更多
关键词 gastritis tongue stomach duodenum correlation of data frequent pattern growth algorithm
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Discovering periodic frequent travel patterns of individual metro passengers considering different time granularities and station attributes 认领 引用 被引量：3
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作者 Zhibin Jiang Yan Tang +2 位作者 Jinjing Gu Zhiqing Zhang Wei Liu 《International Journal of Transportation Science and Technology》 EI 2024年第2期12-26,共15页
Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure.Although conventional algorithms for periodic frequent pattern detection have... Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure.Although conventional algorithms for periodic frequent pattern detection have numerous applications,there is still little research on periodic frequent pattern detection of individual passengers in the metro.The travel behavior of individual passengers has complex spatio-temporal characteristics in the metro network,which may pose new challenges in discovering periodic frequent patterns of individual metro passengers and developing mining algorithms based on real-world smart card data.This study addresses these issues by proposing a novel pattern for metro passenger travel pattern called periodic frequent passenger traffic patterns with time granularities and station attributes(PFPTS).This discovered pattern can automatically capture the features of the temporal dimension(morning and evening peak hours,week)and the spatial dimension(entering and leaving stations).The corresponding complete mining algorithm with the PFPTS-tree structure has been developed.To evaluate the performance of PFPTS-tree,several experiments are conducted on one-year real-world smart card data collected by an automatic fare collection system in a certain large metro network.The results show that PFPTS-Tree is efficient and can discover numerous interesting periodic frequent patterns of metro passengers in the real-world dataset. 展开更多
关键词 Metro passenger travel pattern Spatio-temporal characteristics Periodic frequent pattern PFPTS-tree structure Smart card data
Research and Application on Web Information Retrieval Based on Improved FP-Growth Algorithm 认领 引用 被引量：3
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作者 JIAO Minghai YAN Ping JIANG Huiyan 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第5期1065-1068,共4页
A kind of single linked lists named aggregative chain is introduced to the algorithm, thus improving the architecture of FP tree. The new FP tree is a one-way tree and only the pointers that point its parent at each n... A kind of single linked lists named aggregative chain is introduced to the algorithm, thus improving the architecture of FP tree. The new FP tree is a one-way tree and only the pointers that point its parent at each node are kept. Route information of different nodes in a same item are compressed into aggregative chains so that the frequent patterns will be produced in aggregative chains without generating node links and conditional pattern bases. An example of Web key words retrieval is given to analyze and verify the frequent pattern algorithm in this paper. 展开更多
关键词 data mining chains FP-growth algorithm frequent pattern aggregative information retrieval
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