Relevant bibliographies by topics / Sequential pattern mining

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Sequential pattern mining'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 June 2025

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Journal articles on the topic "Sequential pattern mining"

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Patil, Kirti S., and Sandip S. Patil. "Sequential Pattern Mining Using Algorithm." Asian Journal of Computer Science and Technology 2, no. 1 (2013): 19–21. http://dx.doi.org/10.51983/ajcst-2013.2.1.1715.

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The concept of Sequential Pattern Mining was first introduced by Rakesh Agrawal and Ramakrishnan Srikant in the year 1995. Sequential Patterns are used to discover sequential sub-sequences among large amount of sequential data. In web usage mining, sequential patterns are exploited to find sequential navigation patterns that appear in users’ sessions sequentially. The information obtained from sequential pattern mining can be used in marketing, medical records, sales analysis, and so on. In this paper, a new algorithm is proposed; it combines the Apriori algorithm and FP-tree structure which p
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Yun, Unil, Gwangbum Pyun, and Eunchul Yoon. "Efficient Mining of Robust Closed Weighted Sequential Patterns Without Information Loss." International Journal on Artificial Intelligence Tools 24, no. 01 (2015): 1550007. http://dx.doi.org/10.1142/s0218213015500074.

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Sequential pattern mining has become one of the most important topics in data mining. It has broad applications such as analyzing customer purchase data, Web access patterns, network traffic data, DNA sequencing, and so on. Previous studies have concentrated on reducing redundant patterns among the sequential patterns, and on finding meaningful patterns from huge datasets. In sequential pattern mining, closed sequential pattern mining and weighted sequential pattern mining are the two main approaches to perform mining tasks. This is because closed sequential pattern mining finds representative
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Yu, Xiangzhan, Zhaoxin Zhang, Haining Yu, Feng Jiang, and Wen Ji. "An Asynchronous Periodic Sequential Pattern Mining Algorithm with Multiple Minimum Item Supports for Ad Hoc Networking." Journal of Sensors 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/461659.

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The original sequential pattern mining model only considers occurrence frequencies of sequential patterns, disregarding their occurrence periodicity. We propose an asynchronous periodic sequential pattern mining model to discover the sequential patterns that not only occur frequently but also appear periodically. For this mining model, we propose a pattern-growth mining algorithm to mine asynchronous periodic sequential patterns with multiple minimum item supports. This algorithm employs a divide-and-conquer strategy to mine asynchronous periodic sequential patterns in a depth-first manner rec
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Tran, Duong Huy, Thang Truong Nguyen, Thi Duc Vu, and Anh The Tran. "MINING TOP-K FREQUENT SEQUENTIAL PATTERN IN ITEM INTERVAL EXTENDED SEQUENCE DATABASE." Journal of Computer Science and Cybernetics 34, no. 3 (2018): 249–63. http://dx.doi.org/10.15625/1813-9663/34/3/13053.

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Abstract. Frequent sequential pattern mining in item interval extended sequence database (iSDB) has been one of interesting task in recent years. Unlike classic frequent sequential pattern mining, the pattern mining in iSDB also consider the item interval between successive items; thus, it may extract more meaningful sequential patterns in real life. Most previous frequent sequential pattern mining in iSDB algorithms needs a minimum support threshold (minsup) to perform the mining. However, it’s not easy for users to provide an appropriate threshold in practice. The too high minsup value will
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Bithi, Ashin Ara. "Sequential Pattern Tree Mining." IOSR Journal of Computer Engineering 15, no. 5 (2013): 79–78. http://dx.doi.org/10.9790/0661-1557978.

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Sharma, Vikrant. "Improving Efficiency of High Utility Sequential Pattern Extraction." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 234–42. http://dx.doi.org/10.17762/msea.v70i1.2304.

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Text mining used on texts and publications in the biomedical and molecular biology fields is referred to as "biomedical text mining." It is a relatively new area of study at the intersection of computational linguistics, bioinformatics, and natural language processing. Superior usefulness the goal of sequential pattern mining is to identify statistically significant patterns among data instances when the values are presented sequentially. Time series mining is typically regarded as a distinct activity even if it is closely linked since it is typically assumed that the values are discrete. Stru
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Thiet, Pham Thi. "APPLYING THE ATTRIBUTED PREFIX TREE FOR MINING CLOSED SEQUENTIAL PATTERNS." Vietnam Journal of Science and Technology 54, no. 3A (2018): 106. http://dx.doi.org/10.15625/2525-2518/54/3a/11964.

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Mining closed sequential patterns is one of important tasks in data mining. It is proposed to resolve difficult problems in mining sequential pattern such as mining long frequent sequences that contain a combinatorial number of frequent subsequences or using very low support thresholds to mine sequential patterns is usually both time- and memory-consuming. This paper applies the characteristics of closed sequential patterns and sequence extensions into the prefix tree structure to mine closed sequential patterns from the sequence database. The paper uses the parent–child relationship on prefix
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Ziembiński, Radosław. "Algorithms for Context Based Sequential Pattern Mining." Fundamenta Informaticae 76, no. 4 (2007): 495–510. https://doi.org/10.3233/fun-2007-76405.

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This paper describes practical aspects of a novel approach to the sequential pattern mining named Context Based Sequential Pattern Mining (CBSPM). It introduces a novel ContextMapping algorithm used for the context pattern mining and an illustrative example showing some advantages of the proposed method. The approach presented here takes into consideration some shortcomings of the classic problem of the sequential pattern mining. The significant advantage of the classic sequential patterns mining is simplicity. It introduces simple element construction, built upon set of atomic items. The comp
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Regina, Yulia Yasmin, Saptawati Putri, and Sitohang Benhard. "Progressive Mining of Sequential Patterns Based on Single Constraint." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 2 (2017): 709–17. https://doi.org/10.12928/TELKOMNIKA.v15i2.5098.

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Data that were appeared in the order of time and stored in a sequence database can be processed to obtain sequential patterns. Sequential pattern mining is the process to obtain sequential patterns from database. However, large amount of data with a variety of data type and rapid data growth raise the scalability issue in data mining process. On the other hand, user needs to analyze data based on specific organizational needs. Therefore, constraint is used to impose limitation in the mining process. Constraint in sequential pattern mining can reduce the short and trivial sequential patterns so
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Duong, Tran Huy, Nguyen Truong Thang, Vu Duc Thi, and Tran The Anh. "HIGH UTILITY ITEM INTERVAL SEQUENTIAL PATTERN MINING ALGORITHM." Journal of Computer Science and Cybernetics 36, no. 1 (2020): 1–15. http://dx.doi.org/10.15625/1813-9663/1/1/14398.

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High utility sequential pattern mining is a popular topic in data mining with the main purpose is to extract sequential patterns with high utility in the sequence database. Many recent works have proposed methods to solve this problem. However, most of them does not consider item intervals of sequential patterns which can lead to the extraction of sequential patterns with too long item interval, thus making little sense. In this paper, we propose a High Utility Item Interval Sequential Pattern (HUISP) algorithm to solve this problem. Our algorithm uses pattern growth approach and some techniqu
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Dissertations / Theses on the topic "Sequential pattern mining"

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Mordvanyuk, Natalia. "Efficient sequential and temporal pattern mining." Doctoral thesis, Universitat de Girona, 2021. http://hdl.handle.net/10803/672924.

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The contributions of the present thesis are in the domain of Pattern Mining and Knowledge Discovery, being of particular relevance for the sequential pattern mining and time-interval related pattern mining fields. In this thesis, a new efficient sequential pattern mining algorithm called VEPRECO is introduced, the contributions of which are: (i) a new representation, (ii) pre-pruning strategies and (iii) candidate selection policies which reduce the number of iterations of the algorithm. In this thesis, a new efficient algorithm for mining time interval patterns, called vertTIRP, has als
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Lu, Jing. "From sequential patterns to concurrent branch patterns : a new post sequential patterns mining approach." Thesis, University of Bedfordshire, 2006. http://hdl.handle.net/10547/556399.

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Sequential patterns mining is an important pattern discovery technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been intensively studied and there exists a great diversity of algorithms. However, there is a major problem associated with the conventional sequential patterns mining in that patterns derived are often large and not very easy to understand or use. In addition, more complex relations among events are often hidden behind sequences. A novel model for sequential patterns called Sequential Patterns Graph (SPG) is p
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Baumgarten, Matthias. "Multi-dimensional sequential and associative pattern mining." Thesis, University of Ulster, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412149.

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Bu, Daher Julie. "Sequential Pattern Generalization for Mining Multi-source Data." Electronic Thesis or Diss., Université de Lorraine, 2020. http://www.theses.fr/2020LORR0204.

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La digitalisation de notre monde est souvent associée à une production de grandes quantités de données. Ainsi, des outils de collecte et de stockage de données ont dû être développés, à des fins d’exploitation en recherche ou dans l’industrie. Les données collectées peuvent provenir de plusieurs sources, formant ainsi de gros corpus de données hétérogènes. Ces corpus peuvent être analysés pour extraire de l’information. C’est l’objet de la fouille de données, qui fait l’objet d’un intérêt grandissant depuis de nombreuses années. Différentes approches de fouille de données ont été proposées, pa
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Wu, Sheng-Tang. "Knowledge discovery using pattern taxonomy model in text mining." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16675/.

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In the last decade, many data mining techniques have been proposed for fulfilling various knowledge discovery tasks in order to achieve the goal of retrieving useful information for users. Various types of patterns can then be generated using these techniques, such as sequential patterns, frequent itemsets, and closed and maximum patterns. However, how to effectively exploit the discovered patterns is still an open research issue, especially in the domain of text mining. Most of the text mining methods adopt the keyword-based approach to construct text representations which consist of single w
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Wu, Sheng-Tang. "Knowledge discovery using pattern taxonomy model in text mining." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16675/1/Sheng-Tang_Wu_Thesis.pdf.

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In the last decade, many data mining techniques have been proposed for fulfilling various knowledge discovery tasks in order to achieve the goal of retrieving useful information for users. Various types of patterns can then be generated using these techniques, such as sequential patterns, frequent itemsets, and closed and maximum patterns. However, how to effectively exploit the discovered patterns is still an open research issue, especially in the domain of text mining. Most of the text mining methods adopt the keyword-based approach to construct text representations which consist of single w
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Lau, Cher Han. "Detecting news topics from microblogs using sequential pattern mining." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/68159/1/Cher%20Han_Lau_Thesis.pdf.

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This thesis presents a sequential pattern based model (PMM) to detect news topics from a popular microblogging platform, Twitter. PMM captures key topics and measures their importance using pattern properties and Twitter characteristics. This study shows that PMM outperforms traditional term-based models, and can potentially be implemented as a decision support system. The research contributes to news detection and addresses the challenging issue of extracting information from short and noisy text.
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Onal, Kezban Dilek. "A New Wap-tree Based Sequential Pattern Mining Algorithm For Faster Pattern Extraction." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614638/index.pdf.

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Sequential pattern mining constitutes a basis for solution of problems in various domains like bio-informatics and web usage mining. Research on this field continues seeking faster algorithms. WAP-Tree based algorithms that emerged from web usage mining literature have shown a remarkable performance on single-item sequence databases. In this study, we investigated application of WAP-Tree based mining to multi-item sequential pattern mining and we designed an extension of WAP-Tree data structure for multi-item sequence databases, the MULTI-WAP-Tree. In addition, we propose a new mining strategy
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Zhang, Qi. "The Application of Sequential Pattern Mining in Healthcare Workflow System and an Improved Mining Algorithm Based on Pattern-Growth Approach." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113261.

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Stafford, William B. "Sequential pattern detection and time series models for predicting IED attacks." Thesis, Monterey, Calif. : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Mar/09Mar%5FStafford.pdf.

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Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, March 2009.<br>Thesis Advisor(s): Kamel, Magdi. "March 2009." Description based on title screen as viewed on April 24, 2009. Author(s) subject terms: Sequential Pattern Detection, Time Series, Predicting IED Attacks, Data Mining. Includes bibliographical references (p. 77). Also available in print.
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Books on the topic "Sequential pattern mining"

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Baumgarten, Matthias. Multi-dimensional sequential and associative pattern mining. The Author], 2004.

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Kumar, Pradeep. Pattern discovery using sequence data mining: Applications and studies. Information Science Reference, 2012.

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Neelanarayanan, ed. Personalized e-learning system using learner profile ontology and sequential pattern mining-based recommendation. Association of Scientists, Developers and Faculties, 2014.

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1973-, Wang Wei, and Yang Jiong, eds. Mining sequential patterns from large data sets. Springer, 2005.

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Adamo, Jean-Marc. Data Mining for Association Rules and Sequential Patterns. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4.

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Mining Sequential Patterns from Large Data Sets. Springer-Verlag, 2005. http://dx.doi.org/10.1007/b104937.

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Wei, Wang, and Jiong Yang. Mining Sequential Patterns from Large Data Sets. Springer, 2010.

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Adamo, Jean-Marc. Data Mining for Association Rules and Sequential Patterns. Island Press, 2000.

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Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms. Springer New York, 2001.

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Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms. Springer, 2000.

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Book chapters on the topic "Sequential pattern mining"

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Shen, Wei, Jianyong Wang, and Jiawei Han. "Sequential Pattern Mining." In Frequent Pattern Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_11.

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Li, Tian-Rui, Yang Xu, Da Ruan, and Wu-ming Pan. "Sequential Pattern Mining*." In Intelligent Data Mining. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11004011_5.

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Huynh, Ut, Bac Le, and Duy-Tai Dinh. "Hiding Periodic High-Utility Sequential Patterns." In Periodic Pattern Mining. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3964-7_10.

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Yen, Show-Jane, Yue-Shi Lee, Bai-En Shie, and Yeuan-Kuen Lee. "Mining Sequential Patterns with Pattern Constraint." In Intelligent Information and Database Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15702-3_58.

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Wu, Youxi, Meng Geng, Yan Li, Lei Guo, and Philippe Fournier-Viger. "NetHAPP: High Average Utility Periodic Gapped Sequential Pattern Mining." In Periodic Pattern Mining. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3964-7_11.

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Huynh, Huy Minh, Nam Ngoc Pham, Zuzana Komínková Oplatková, Loan Thi Thuy Nguyen, and Bay Vo. "Sequential Pattern Mining Using IDLists." In Computational Collective Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63007-2_27.

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Muzammal, Muhammad, and Rajeev Raman. "Uncertainty in Sequential Pattern Mining." In Data Security and Security Data. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25704-9_18.

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Ye, Zhenqiang, Ziyang Li, Weibin Guo, Wensheng Gan, Shicheng Wan, and Jiahui Chen. "Fast Weighted Sequential Pattern Mining." In Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08530-7_68.

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Huynh, Ut, Bac Le, Duy-Tai Dinh, and Van-Nam Huynh. "Mining Periodic High-Utility Sequential Patterns with Negative Unit Profits." In Periodic Pattern Mining. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3964-7_9.

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Genga, Laura, Domenico Potena, Andrea Chiorrini, Claudia Diamantini, and Nicola Zannone. "A Latitudinal Study on the Use of Sequential and Concurrency Patterns in Deviance Mining." In Complex Pattern Mining. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36617-9_7.

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Conference papers on the topic "Sequential pattern mining"

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Wang, Xilu, and Weili Yao. "Sequential Pattern Mining: Optimum Maximum Sequential Patterns and Consistent Sequential Patterns." In 2007 IEEE International Conference on Integration Technology. IEEE, 2007. http://dx.doi.org/10.1109/icitechnology.2007.4290497.

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Rabatel, Julien, Sandra Bringay, and Pascal Poncelet. "Contextual Sequential Pattern Mining." In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.182.

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Jabbour, Said, Jerry Lonlac, and Lakhdar Sais. "Mining Gradual Itemsets Using Sequential Pattern Mining." In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8858864.

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Songram, P., V. Boonjing, and S. Intakosum. "Closed Multidimensional Sequential Pattern Mining." In Third International Conference on Information Technology: New Generations (ITNG'06). IEEE, 2006. http://dx.doi.org/10.1109/itng.2006.41.

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Huang, Jen-Wei, Chi-Yao Tseng, Jian-Chih Ou, and Ming-Syan Chen. "On progressive sequential pattern mining." In the 15th ACM international conference. ACM Press, 2006. http://dx.doi.org/10.1145/1183614.1183762.

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Yusheng, Xu, Zhang Lanhui, Ma Zhixin, Li Lian, Xiaoyun Chen, and Tharam S. Dillon. "Mining Sequential Pattern Using DF2Ls." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.29.

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Pinto, Helen, Jiawei Han, Jian Pei, Ke Wang, Qiming Chen, and Umeshwar Dayal. "Multi-dimensional sequential pattern mining." In the tenth international conference. ACM Press, 2001. http://dx.doi.org/10.1145/502585.502600.

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Zhou, Tingfu, Wensheng Gan, and Zhenlian Qi. "Weighted Contiguous Sequential Pattern Mining." In 2022 4th International Conference on Data Intelligence and Security (ICDIS). IEEE, 2022. http://dx.doi.org/10.1109/icdis55630.2022.00061.

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Xie, Fei, Xindong Wu, Xuegang Hu, et al. "Sequential Pattern Mining with Wildcards." In 2010 22nd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2010. http://dx.doi.org/10.1109/ictai.2010.42.

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"MINING SEQUENTIAL PATTERNS WITH REGULAR EXPRESSION CONSTRAINTS USING SEQUENTIAL PATTERN TREE." In 6th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2004. http://dx.doi.org/10.5220/0002621601160121.

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