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TaSPM: Targeted sequential pattern Mining

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2024年第5期18卷 1-18页

作者： Huang, Gengsen Gan, Wensheng Yu, Philip S. Jinan Univ Guangzhou Peoples R China Univ Illinois Chicago IL USA

sequential pattern mining (SPM) is an important technique in the field of pattern mining, which has many applications in reality. Although many efficient SPM algorithms have been proposed, there are few studies that can focus on targeted tasks. Targeted querying of the concerned sequential patterns can not only reduce the number of patterns generated, but also increase the efficiency of users in performing related analysis. The current algorithms available for targeted sequence querying are based on specific scenarios and can not be extended to other applications. In this article, we formulate the problem of targeted sequential pattern mining and propose a generic algorithm, namely TaSPM. What is more, to improve the efficiency of TaSPM on large-scale datasets and multiple-item-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in the mining process. Totally four pruning strategies are designed in TaSPM, and hence TaSPM can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conducted extensive experiments on different datasets to compare the baseline SPM algorithm with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.

关键词： Data mining targeted querying sequential pattern target sequence

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Construction of Stretching-Bending sequential pattern to Recognize Work Cycles for Earthmoving Excavator from Long Video Sequences

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SENSORS 2021年第10期21卷 3427-3427页

作者： Wu, Yiguang Wang, Meizhen Liu, Xuejun Wang, Ziran Ma, Tianwu Xie, Yujia Li, Xiuquan Wang, Xing Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210023 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Peoples R China Nanjing Normal Univ Sch Geog Nanjing 210023 Peoples R China Nanjing Normal Univ Sch Informat Engn Taizhou Coll Taizhou 225300 Peoples R China Nanjing Univ Finance & Econ Coll Informat Engn Nanjing 210023 Peoples R China

Counting the number of work cycles per unit of time of earthmoving excavators is essential in order to calculate their productivity in earthmoving projects. The existing methods based on computer vision (CV) find it difficult to recognize the work cycles of earthmoving excavators effectively in long video sequences. Even the most advanced sequential pattern-based approach finds recognition difficult because it has to discern many atomic actions with a similar visual appearance. In this paper, we combine atomic actions with a similar visual appearance to build a stretching-bending sequential pattern (SBSP) containing only "Stretching" and "Bending" atomic actions. These two atomic actions are recognized using a deep learning-based single-shot detector (SSD). The intersection over union (IOU) is used to associate atomic actions to recognize the work cycle. In addition, we consider the impact of reality factors (such as driver misoperation) on work cycle recognition, which has been neglected in existing studies. We propose to use the time required to transform "Stretching" to "Bending" in the work cycle to filter out abnormal work cycles caused by driver misoperation. A case study is used to evaluate the proposed method. The results show that SBSP can effectively recognize the work cycles of earthmoving excavators in real time in long video sequences and has the ability to calculate the productivity of earthmoving excavators accurately.

关键词： earthmoving projects earthmoving excavators surveillance cameras reality factors deep learning work cycle atomic action sequential pattern long video sequences computer vision

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Significance-based discriminative sequential pattern mining

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 122卷 54-64页

作者： He, Zengyou Zhang, Simeng Wu, Jun Dalian Univ Technol Sch Software Tuqiang Rd Dalian Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Tuqiang Rd 321 Dalian 116600 Peoples R China Zunyi Normal Univ Sch Informat Engn Zunyi Peoples R China

Discriminative sequential patterns are sub-sequences whose occurrences exhibit significant differences across sequential data sets with different class labels. The discovery of such types of patterns has many practical applications in different fields. To date, various algorithms for mining discriminative sequential patterns have been proposed. However, the reported patterns from these methods usually contain many false positives that only hold in the sample data by chance. To alleviate this issue, we put forward the concept of significance-based discriminative sequential pattern mining and a corresponding algorithm DSPM-MTC (Discriminative sequential pattern Mining with Multiple Testing Correction). The key idea of DSPM-MTC is to integrate the multiple hypothesis testing correction procedure into the pattern mining process to generate a pattern set with error rate control. To demonstrate the effectiveness of DSPM-MTC, we conduct a series of experiments on real sequential data sets and simulation data sets. The experimental results show that DSPM-MTC can effectively recognize false discoveries to generate a pattern set with statistical quality control. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： sequential pattern Discriminative pattern Multiple hypothesis testing Family-wise error rate False discovery rate

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A Survey of Parallel sequential pattern Mining

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2019年第3期13卷 25-25页

作者： Gan, Wensheng Lin, Jerry Chun-Wei Fournier-Viger, Philippe Chao, Han-Chieh Yu, Philip S. Harbin Inst Technol Shenzhen Hit Campus Univ Town Shenzhen Shenzhen 518055 Peoples R China Western Norway Univ Appl Sci Inndalsveien 28 N-5063 Bergen Norway Natl Dong Hwa Univ 1Sec 2Da Hsueh Rd Hualien 97401 Taiwan Univ Illinons Chicago 1200 W Harrison St Chicago IL 60607 USA

With the growing popularity of shared resources, large volumes of complex data of different types are collected automatically. Traditional data mining algorithms generally have problems and challenges including huge memory cost, low processing speed, and inadequate hard disk space. As a fundamental task of data mining, sequential pattern mining (SPM) is used in a wide variety of real-life applications. However, it is more complex and challenging than other pattern mining tasks, i.e., frequent itemset mining and association rule mining, and also suffers from the above challenges when handling the large-scale data. To solve these problems, mining sequential patterns in a parallel or distributed computing environment has emerged as an important issue with many applications. In this article, an in-depth survey of the current status of parallel SPM (PSPM) is investigated and provided, including detailed categorization of traditional serial SPM approaches, and state-of-the art PSPM. We review the related work of PSPM in details including partition-based algorithms for PSPM, apriori-based PSPM, pattern-growth-based PSPM, and hybrid algorithms for PSPM, and provide deep description (i.e., characteristics, advantages, disadvantages, and summarization) of these parallel approaches of PSPM. Some advanced topics for PSPM, including parallel quantitative/weighted/utility SPM, PSPM from uncertain data and stream data, hardware acceleration for PSPM, are further reviewed in details. Besides, we review and provide some well-known open-source software of PSPM. Finally, we summarize some challenges and opportunities of PSPM in the big data era.

关键词： Data science big data data mining parallelism sequential pattern

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Hybrid traffic identification based on sequential pattern mining 5

Hybrid traffic identification based on sequential pattern mi...

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5th International Conference on Information Science and Control Engineering (ICISCE)

作者： Fang, Fang Ye, Chunming Anhui Univ Chinese Med Sch Med Informat Technol Hefei Anhui Peoples R China Natl Univ Def Technol Hefei Anhui Peoples R China

ISBN: (纸本)9781538655009

A novel identification method based on sequential pattern mining is proposed to detect different network traffic in encrypted hybrid data. After dimensionality reduction for traffic series, the data are cut into short sequences. By sequential pattern mining and pruning, the characteristics sequential sets, which are extracted from transaction database comprised of these sequences, are used to calculated and analyzed the identification results. With removing each traffic's characteristic away from the hybrid data, the final results are verified and concluded. The experiments show that the recall and precision of identification results are more than 60%.

关键词： hybrid traffic identification sequential pattern characteristics sequential sets

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sequential-index pattern mining for lifecare telecommunication platform

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4期22卷 1039-1048页

作者： Kim, Joo-Chang Chung, Kyungyong Kyonggi Univ Dept Comp Sci Data Min Lab 154-42 Gwanggyosan Ro Suwon 16227 Gyeonggi Do South Korea Kyonggi Univ Div Comp Sci & Engn 154-42 Gwanggyosan Ro Suwon 16227 Gyeonggi Do South Korea

Lifecare for quality-of-life improvement includes not only the prevention and treatment of diseases and health management anywhere and at any-time, but also the improvement of lifestyle, the psychological environment, and the emergency response. The construction of a sustainable model on the telecommunications platform as a solution has been constantly studied using intelligence information technology. In this study, sequential-index pattern mining on the lifecare telecommunications platform is proposed. The method is as follows. Various temporal and regional indices that are provided by the Meteorological Administration, Meteorological Data Open Portal, Public Data Portal, Healthcare Big Data Hub, and Korea Environment Corporation are collected as lifecare index data. The collected index data are transmitted to the NAS file server of the lifecare telecommunications platform using peer-to-peer (P2P)-based interconnection technology and high-performance computing. The sequential relationships among the indices are discovered to consider the semantic relationships. Using the AprioriAll mining algorithm, the maximum sequence is determined to find sequential relationships in the frequent sequence set. To process the pattern extraction and computation efficiently, recomposed transactions are stored in the high-performance NAS file server. Also, a mining-index prediction model is developed in consideration of the sequential relationships to complement the weakness of the statistical time-series analysis. Since the regional meteorological conditions are similar, the index sequential pattern can solve the problem of the generation of omissions or errors regarding the real-time monitoring data that are due to the monitoring equipment or communication failures. Therefore, the proposed sequential-index pattern mining can be utilized as a lifecare tool for risk-factor detection, risk prediction, and trend analysis.

关键词： sequential pattern Data mining Lifecare Telecommunication platform

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Algorithms of sequential pattern Generation with Noise using Stochastic and Fuzzy Models 2nd

Algorithms of Sequential Pattern Generation with Noise using...

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2nd International Scientific Conference on Intelligent Information Technologies for Industry (IITI)

作者： Butakova, Maria A. Chernov, Andrey V. Guda, Alexander N. Rostov State Transport Univ Rostov Na Donu Russia

ISBN: (纸本)9783319683218

A task of sequential pattern generation can be considered as a problem which is inverse to sequential pattern mining. This paper presents two novel approaches to the sequential pattern generation with noise, namely the approach based on stochastic automata and context-free grammars and the approach based on Hidden Markov model. The distinctive feature of these methods is the suitability to produce an output in the noisy and fuzzy input data. Also, we present the detailed calculation algorithms to the proposed approaches.

关键词： sequential pattern Stochastic automata model Hidden markov model Fuzzy pattern

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sequential pattern Based Temporal Contour Representations for Content-Based Multimedia Timeline Analysis 15

Sequential Pattern Based Temporal Contour Representations fo...

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15th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Ren, Gang Johnson, Joseph Lee, Hyunhwan Ogihara, Mitsunori Univ Miami Ctr Computat Sci Coral Gables FL 33146 USA Univ Miami Sch Business Adm Dept Mkt Coral Gables FL 33146 USA Univ Miami Dept Comp Sci Coral Gables FL 33124 USA

ISBN: (纸本)9781509061679

Temporal contour shapes are closely linked to the narrative structure of multimedia content and provide important reference points in content-based multimedia timeline analysis. In this paper, multimedia timeline is extracted from content as time varying video and audio signal features. A temporal contour representation is implemented based on sequential pattern discovery algorithm for modeling the variation contours of multimedia features. The proposed contour representation extracts repetitive temporal patterns from a hierarchy of time resolutions or from synchronized video/audio feature dimensions. The statistically significant contour components, depicting the dominant timeline shapes, are utilized as a structural or analytical representation of the timeline. The modeling performance of this proposed temporal modeling framework is demonstrated through empirical validation and subjective evaluations.

关键词： sequential pattern multimedia structure analysis multimedia signal processing contour representations

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A pattern growth-based sequential pattern mining algorithm called prefixSuffixSpan

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2017年第12期4卷 1-14页

作者： Belise, Kenmogne Edith Calvin, Tadmon Roger, Nkambou LIFA Fac Sci Dept Math & Comp Sci POB 67 Dschang Cameroon Univ Quebec Comp Sci Dept Knowledge Management Lab 201 Ave President Kennedy Montreal PQ H2X 3Y7 Canada

sequential pattern mining is an important data mining problem widely addressed by the data mining community, with a very large field of applications. The sequence pattern mining aims at extracting a set of attributes, shared across time among a large number of objects in a given database. The work presented in this paper is directed towards the general theoretical foundations of the pattern-growth approach. It helps indepth understanding of the pattern-growth approach, current status of provided solutions, and direction of research in this area. In this paper, this study is carried out on a particular class of pattern-growth algorithms for which patterns are grown by making grow either the current pattern prefix or the current pattern suffix from the same position at each growth-step. This study leads to a new algorithm called prefixSuffixSpan. Its correctness is proven and experimentations are performed.

关键词： sequence mining sequential pattern pattern-growth direction pattern-growth ordering search space pruning partitioning

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An effective contrast sequential pattern mining approach to taxpayer behavior analysis

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2016年第4期19卷 633-651页

作者： Zheng, Zhigang Wei, Wei Liu, Chunming Cao, Wei Cao, Longbing Bhatia, Maninder Univ Technol Sydney Fac Engn & Informat Technol Adv Analyt Inst Sydney NSW 2007 Australia Australian Taxat Off Sydney NSW Australia

Data mining for client behavior analysis has become increasingly important in business, however further analysis on transactions and sequential behaviors would be of even greater value, especially in the financial service industry, such as banking and insurance, government and so on. In a real-world business application of taxation debt collection, in order to understand the internal relationship between taxpayers' sequential behaviors (payment, lodgment and actions) and compliance to their debt, we need to find the contrast sequential behavior patterns between compliant and non-compliant taxpayers. Contrast patterns (CP) are defined as the itemsets showing the difference/discrimination between two classes/datasets (Dong and Li, 1999). However, the existing CP mining methods which can only mine itemset patterns, are not suitable for mining sequential patterns, such as time-ordered transactions in taxpayer sequential behaviors. Little work has been conducted on Contrast sequential pattern (CSP) mining so far. Therefore, to address this issue, we develop a CSP mining approach, e C S P, by using an effective CSP-tree structure, which improves the PrefixSpan tree (Pei et al., 2001) for mining contrast patterns. We propose some heuristics and interestingness filtering criteria, and integrate them into the CSP-tree seamlessly to reduce the search space and to find business-interesting patterns as well. The performance of the proposed approach is evaluated on three real-world datasets. In addition, we use a case study to show how to implement the approach to analyse taxpayer behaviour. The results show a very promising performance and convincing business value.

关键词： Contrast pattern sequential pattern Client behavior analysis

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