Relevant bibliographies by topics / Mining pattern

Contents

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

Academic literature on the topic 'Mining pattern'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Mining pattern"

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S, Abirami. "Pattern-Growth Methods for Frequent Pattern Mining." Shanlax International Journal of Arts, Science and Humanities 6, S1 (2018): 76–81. https://doi.org/10.5281/zenodo.1410989.

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Mining frequent patterns from large databases play an essential role in many data mining tasks and has broad applications. Most of the previously proposed methods adopt Apriori-like candidate-generation-and-test approaches. However, those methods  may  encounter  serious  challenges  when  mining  datasets  with  prolific patterns and long patterns.In this work, to develop a class of novel and efficient pattern-growth methods for mining various frequent patterns from large databases. Pattern-growth methods  adopt a divi
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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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Han, Jiawei, and Jian Pei. "Mining frequent patterns by pattern-growth." ACM SIGKDD Explorations Newsletter 2, no. 2 (2000): 14–20. http://dx.doi.org/10.1145/380995.381002.

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Chai, Xin, Dan Yang, Jingyu Liu, Yan Li, and Youxi Wu. "Top-k sequence pattern mining with non-overlapping condition." Filomat 32, no. 5 (2018): 1703–10. http://dx.doi.org/10.2298/fil1805703c.

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Pattern mining has been widely applied in many fields. Users often mine a large number of patterns. However, most of these are difficult to apply in real applications. Top-k pattern mining, which involves finding the most frequent k patterns, is an effective strategy, because the more frequently a pattern occurs, the more likely they are to be important for users. However, top-k mining can only mine short patterns in mining applications with the Apriori property. It is well-known that short patterns contain less information than long patterns. In this paper, we focus on mining top-k sequence p
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Harco, Leslie Hendric Spits Warnars, Trisetyarso Agung, and Randriatoamanana Richard. "Confidence of AOI-HEP Mining Pattern." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 3 (2018): 1217–25. https://doi.org/10.12928/TELKOMNIKA.v16i3.5303.

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Attribute Oriented Induction High level Emerging Pattern (AOI-HEP) has been proven can mine frequent and similar patterns and the finding AOI-HEP patterns will be underlined with confidence mining pattern for each AOI-HEP pattern either frequent or similar pattern, and each dataset as confidence AOIHEP pattern between frequent and similar patterns. Confidence per AOI-HEP pattern will show how interested each of AOI-HEP pattern, whilst confidende per dataset will show how interested each dataset between frequent and similar patterns. The experiments for finding confidence of each AOI-HEP patter
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Han, Jia-Wei, Jian Pei, and Xi-Feng Yan. "From sequential pattern mining to structured pattern mining: A pattern-growth approach." Journal of Computer Science and Technology 19, no. 3 (2004): 257–79. http://dx.doi.org/10.1007/bf02944897.

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S., Sivaranjani. "Detecting Congestion Patterns in Spatio Temporal Traffic Data Using Frequent Pattern Mining." Bonfring International Journal of Networking Technologies and Applications 5, no. 1 (2018): 21–23. http://dx.doi.org/10.9756/bijnta.8372.

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Aida Jusoh, Julaily, Mustafa Man, and Wan Aezwani Wan Abu Bakar. "Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset." International Journal of Engineering & Technology 7, no. 4.1 (2018): 134. http://dx.doi.org/10.14419/ijet.v7i4.1.28241.

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Pattern mining refers to a subfield of data mining that uncovers interesting, unexpected, and useful patterns from transaction databases. Such patterns reflect frequent and infrequent patterns. An abundant literature has dedicated in frequent pattern mining and tremendous efficient algorithms for frequent itemset mining in the transaction database. Nonetheless, the infrequent pattern mining has emerged to be an interesting issue in discovering patterns that rarely occur in the transaction database. More researchers reckon that rare pattern occurrences may offer valuable information in knowledg
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Xue, Linyan, Xiaoke Zhang, Fei Xie, Shuang Liu, and Peng Lin. "Frequent Patterns Algorithm of Biological Sequences based on Pattern Prefix-tree." International Journal of Computers Communications & Control 14, no. 4 (2019): 574–89. http://dx.doi.org/10.15837/ijccc.2019.4.3607.

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In the application of bioinformatics, the existing algorithms cannot be directly and efficiently implement sequence pattern mining. Two fast and efficient biological sequence pattern mining algorithms for biological single sequence and multiple sequences are proposed in this paper. The concept of the basic pattern is proposed, and on the basis of mining frequent basic patterns, the frequent pattern is excavated by constructing prefix trees for frequent basic patterns. The proposed algorithms implement rapid mining of frequent patterns of biological sequences based on pattern prefix trees. In e
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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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Dissertations / Theses on the topic "Mining pattern"

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Jagannath, Sandhya. "Utility Guided Pattern Mining." NCSU, 2003. http://www.lib.ncsu.edu/theses/available/etd-11262003-131929/.

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This work is an initial exploration of the use of the decision-theoretic concept of utility to guide pattern mining. We present the use of utility functions as against thresholds and constraints as the mechanism to express user preferences and formulate several pattern mining problems that use utility functions. Utility guided pattern mining provides the twin benefits of capturing user preferences precisely using utility functions and of expressing user focus by choosing an appropriate utility guided pattern mining problem. It addresses the drawbacks of threshold guided pattern mining, the spe
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Shang, Xuequn. "SQL based frequent pattern mining." [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=975449176.

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Coussat, Aurélien. "Pattern Mining in Uncertain Tensors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265629.

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Data mining is the art of extracting information from data and creating useful knowledge. Itemset mining, or pattern mining, is an important subfield that consists in finding relevant patterns in datasets. We focus on two subproblems: high-utility itemset mining, where a numerical value called utility is associated to every tuple of the dataset, and patterns are extracted whose utilities sum up to a high-enough value; and skypattern mining, which is the extraction of patterns optimizing various measures, using the notion of Pareto domination. To tackle both of these challenges, we follow a gen
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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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Lattner, Andreas D. "Temporal pattern mining in dynamic environments /." Berlin : Akad. Verl.-Ges. Aka, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=2995598&prov=M&dok_var=1&dok_ext=htm.

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Termier, Alexandre. "Pattern mining rock: more, faster, better." Habilitation à diriger des recherches, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-01006195.

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Le pattern mining est un domaine du data mining dont le but est l'extraction de régularité dans les données. Ce document présente nos contributions au domaine selon 3 axes : 1. Le domaine du pattern mining est jeune et il y existe encore beaucoup de types de régularités qu'un analyste serait intéressé de découvrir mais qui ne sont pas encore gérées. Nous avons contribué à deux nouveaux types de patterns: les patterns graduels et les patterns périodiques avec "ruptures". Nous avons aussi proposé ParaMiner, un algorithme original pour le pattern mining générique, qui permet à des analystes de sp
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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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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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Pipanmaekaporn, Luepol. "A data mining framework for relevance feature discovery." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/62857/1/Luepol_Pipanmaekaporn_Thesis.pdf.

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This thesis is a study for automatic discovery of text features for describing user information needs. It presents an innovative data-mining approach that discovers useful knowledge from both relevance and non-relevance feedback information. The proposed approach can largely reduce noises in discovered patterns and significantly improve the performance of text mining systems. This study provides a promising method for the study of Data Mining and Web Intelligence.
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Books on the topic "Mining pattern"

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Kiran, R. Uday, Philippe Fournier-Viger, Jose M. Luna, Jerry Chun-Wei Lin, and Anirban Mondal, eds. Periodic Pattern Mining. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3964-7.

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Appice, Annalisa, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, and Zbigniew W. Ras, eds. Complex Pattern Mining. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36617-9.

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Aggarwal, Charu C., and Jiawei Han, eds. Frequent Pattern Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2.

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Fournier-Viger, Philippe, Jerry Chun-Wei Lin, Roger Nkambou, Bay Vo, and Vincent S. Tseng, eds. High-Utility Pattern Mining. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04921-8.

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Ventura, Sebastián, and José María Luna. Supervised Descriptive Pattern Mining. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98140-6.

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Rage, Uday Kiran. Hands-on Pattern Mining. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-6791-8.

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Ventura, Sebastián, and José María Luna. Pattern Mining with Evolutionary Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33858-3.

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Singh, Sameer, Maneesha Singh, Chid Apte, and Petra Perner, eds. Pattern Recognition and Data Mining. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188.

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Yadav, Vikash, Anil Kumar Dubey, Harivans Pratap Singh, Gaurav Dubey, and Erma Suryani. Process Mining Techniques for Pattern Recognition. CRC Press, 2022. http://dx.doi.org/10.1201/9781003169550.

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Lattner, Andreas D. Temporal pattern mining in dynamic environments. IOS Press, 2007.

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Book chapters on the topic "Mining pattern"

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Nijssen, Siegfried. "Pattern Mining." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_600.

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Goertzel, Ben, Cassio Pennachin, and Nil Geisweiller. "Pattern Mining." In Atlantis Thinking Machines. Atlantis Press, 2014. http://dx.doi.org/10.2991/978-94-6239-030-0_19.

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Aggarwal, Charu C. "Association Pattern Mining." In Data Mining. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_4.

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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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Cheng, Hong, Xifeng Yan, and Jiawei Han. "Mining Graph Patterns." In Frequent Pattern Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_13.

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Zhu, Feida. "Mining Long Patterns." In Frequent Pattern Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_4.

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Zhou, Huiyu, Kaoru Shimada, Shingo Mabu, and Kotaro Hirasawa. "Sequence Pattern Mining." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01088-0_2.

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Liu, Guimei. "Max-Pattern Mining." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_216-2.

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Uno, Takeaki. "Frequent Pattern Mining." In Encyclopedia of Algorithms. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_722.

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Millham, Richard, Israel Edem Agbehadji, and Hongji Yang. "Pattern Mining Algorithms." In Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6695-0_4.

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Conference papers on the topic "Mining pattern"

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Li, Ling, Wiktor Zuba, Grigorios Loukides, Solon P. Pissis, and Maria Matsangidou. "Scalable Order-Preserving Pattern Mining." In 2024 IEEE International Conference on Data Mining (ICDM). IEEE, 2024. https://doi.org/10.1109/icdm59182.2024.00028.

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Akado, Yuma, Sakurako Kogure, Alice Sasabe, Jei-Hee Hong, Keishi Saruwatari, and Takashi Iba. "Five patterns for designing pattern mining workshops." In EuroPLoP 2015: 20th European Conference on Pattern Languages of Programs. ACM, 2015. http://dx.doi.org/10.1145/2855321.2855331.

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Giannotti, Fosca, Mirco Nanni, Fabio Pinelli, and Dino Pedreschi. "Trajectory pattern mining." In the 13th ACM SIGKDD international conference. ACM Press, 2007. http://dx.doi.org/10.1145/1281192.1281230.

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Song, Xiaoli, XiaoTong Wang, and Xiaohua Hu. "Semantic pattern mining for text mining." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840600.

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Zhu, Feida, Xifeng Yan, Jiawei Han, Philip S. Yu, and Hong Cheng. "Mining Colossal Frequent Patterns by Core Pattern Fusion." In 2007 IEEE 23rd International Conference on Data Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icde.2007.367916.

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Lui, Thomas W. H., and David K. Y. Chiu. "NHOP: Detecting descriptive patterns using association pattern mining." In 2008 IEEE International Conference on Granular Computing (GrC-2008). IEEE, 2008. http://dx.doi.org/10.1109/grc.2008.4664672.

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"Trajectory Pattern Mining in Practice - Algorithms for Mining Flock Patterns from Trajectories." In International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004543401430151.

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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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Roy, Suman, Ripunjoy Bordoloi, Kayboy Jyoti Das, Santosh Kumar, and Monoj Kumar Muchahari. "Association Rule Mining on Crime Pattern Mining." In 2021 International Conference on Computational Performance Evaluation (ComPE). IEEE, 2021. http://dx.doi.org/10.1109/compe53109.2021.9752393.

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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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Reports on the topic "Mining pattern"

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Shekhar, Shashi, Pradeep Mohan, Dev Oliver, and Xun Zhou. Crime Pattern Analysis: A Spatial Frequent Pattern Mining Approach. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada561517.

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Kamath, C., and R. Musick. Scalable pattern recognition for large-scale scientific data mining. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/310913.

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Gade, Krishna, Jianyong Wang, and George Karypis. Efficient Closed Pattern Mining in the Presence of Tough Block Constraints. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada439408.

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Baldwin, C., C. Kamath, and R. Musick. An LLNL perspective on ASCI data mining and pattern recognition requirements. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/9659.

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Kamath, C. LDRD 99-ERI-010 Final Report: Sapphire: Scalable Pattern Recognition for Large-Scale Scientific Data Mining. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/15003138.

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Pou, Jose, Jeff Duffany, and Alfredo Cruz. Terrorist Activity Evaluation and Pattern Detection (TAE&PD) in Afghanistan: A Knowledge Discovery and Data Mining (KDDM) Approach for Counter-Terrorism. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada581564.

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Wojciechowski, M. J. Recherche et développement dans le secteur des minéraux. Natural Resources Canada/CMSS/Information Management, 1989. http://dx.doi.org/10.4095/331554.

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Canada has the potential for good long-term development of its mineral resources, and needs to maintain a competitive position for crude minerals in export markets. Therefore, Canada should strongly support the mineral exploration and mining sectors. This conclusion is reinforced by the finding that most of the other countries in this study, which represent much of the world's mining technology and mining education expertise, are in or are approaching the decline phase of their mining industries' life cycles. They are also dependent on imported crude minerals, and are turning their R&D
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Shepherd, Andrew. Zambian Poverty Dynamics and Climate Resilience: A Growing Policy Agenda Through a Period of Crises. Institute of Development Studies, 2025. https://doi.org/10.19088/cpan.2025.008.

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This report synthesises the key research findings of the Zambia Poverty Dynamics programme since the last national report in 2021, whose key findings and recommendations are summarised in Box 1.1. Many dimensions have remained the same; however, the main changes include: (1) a dramatic reversal in urban poverty reduction; (2) a very significant increase in new policy developments, especially in human development, although not yet in ‘growth from below’, but significant progress was achieved in fisheries with the return of fishing ban periods each year on major rivers and lakes to allow fish st
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Stewart, D. B., and B. W. Konda. Summary report of cooperative USBM/CANMET research of methane flow patterns on a longwall coal mining face. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1986. http://dx.doi.org/10.4095/304916.

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Bourbakis, Despina. Detecting and Mining Similarities, Differences and Target Patterns in Sequences of Images Using the PFF, LGG and SPNG Approaches. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada424553.

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