Relevant bibliographies by topics / Generalized sequential pattern

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

Academic literature on the topic 'Generalized sequential pattern'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Generalized sequential pattern"

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Kurniawan, Albert. "Sequential Pattern Mining Data Transaksi Penjualan Supermarket menggunakan Algoritme Generalized Sequential Pattern." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 9, no. 1 (2022): 126–36. http://dx.doi.org/10.35957/jatisi.v9i1.1460.

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Data transaksi penjualan supermarket online merupakan sequence dataset. Data ini menyimpan data transaksi pembelian yang dilakukan oleh pelanggan, sehingga dapat dianalisis menggunakan pendekatan Market Basket Analysis (MBA). Masalah yang sering dialami oleh pihak supermarket adalah sulitnya menerapkan strategi penjualan yang akurat kepada para konsumen. Berdasarkan masalah tersebut, pada penelitian ini akan dilakukan analisis terhadap dataset supermarket West Superstore berdasarkan pendekatan MBA. Algoritme yang digunakan adalah algoritme Generalized Sequential Pattern (GSP), di mana algoritm
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Astuti, Tri, and Lisdya Anggraini. "Analysis of Sequential Book Loan Data Pattern Using Generalized Sequential Pattern (GSP) Algorithm." IJIIS: International Journal of Informatics and Information Systems 2, no. 1 (2019): 17–23. http://dx.doi.org/10.47738/ijiis.v2i1.10.

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As a center for learning and information services, STMIK Amikom Purwokerto Library is a source of learning and a source of intellectual activity that is very important for the entire academic community in supporting the achievement of the college Tridharma program. Book lending transaction data, can produce information that is important as supporting decision making when further analyzed. One useful information is that it can provide information in the form of user behavior patterns in borrowing books that are used to maintain the availability of related book stocks to be balanced. This study
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Al-Refaie, Abbas, Banan Abu Hamdieh, and Natalija Lepkova. "Prediction of Maintenance Activities Using Generalized Sequential Pattern and Association Rules in Data Mining." Buildings 13, no. 4 (2023): 946. http://dx.doi.org/10.3390/buildings13040946.

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This study proposed a data mining framework for predicting sequential patterns of maintenance activities. The framework consisted of data collection, prediction of maintenance activities with and without attributes, and then the comparison between prediction results. In data collection, historical data were collected regarding maintenance activities and product attributes. The generalized sequential pattern (GSP) and association rules were then applied to predict maintenance activities with and without attributes to determine the frequent sequential patterns and significant rules of maintenanc
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Setiawan, Esther Irawati, Valerynta Natalie, Joan Santoso, and Kimiya Fujisawa. "Sequential Pattern Mining to Support Customer Relationship Management at Beauty Clinics." Bulletin of Social Informatics Theory and Application 6, no. 2 (2022): 168–76. http://dx.doi.org/10.31763/businta.v6i2.602.

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 The increasing competition for beauty clinics, makes management need to think of methods to survive in this competition. For that, the company needs to improve CRM in its service to customers. Customer Relationship Management is a series of activities managed in an effort to better understand, attract attention, and maintain customer loyalty.
 Sequential Pattern Mining is one of the data mining techniques that is useful for finding patterns sequential / sequence of a set of items. The algorithm that is used is the Generalized Sequential Pattern (GSP). GSP perfo
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Ramos, Somya, Winarko Edi, and Priyanta Sigit. "A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3422~3432. https://doi.org/10.11591/eei.v11i6.4021.

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In the future, the quality of product suggestions in online retailers will influence client purchasing decisions. Unqualified product suggestions can result in two sorts of errors: false negatives and false positives. Customers may not return to the online store as a result of this. By merging sales transaction data and consumer behavior data in clickstream data format, this work offers a hybrid recommender system in an online store utilizing sequential pattern mining (SPM). Based on the clickstream data components, the product data whose status is only observed by consumers is assessed using
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Wijaya, Raven, and Ramos Somya. "Analisis Dataset Transaksi Penjualan Minimarket Menggunakan Algoritma Generalized Sequential Pattern Berbasis Web." Jurnal Pendidikan Teknologi Informasi (JUKANTI) 5, no. 2 (2022): 8–15. http://dx.doi.org/10.37792/jukanti.v5i2.516.

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Minimarket merupakan salah satu bidang usaha ritel yang menjual berbagai macam barang. Minimarket dapat dijumpai di berbagai tempat seperti di stasiun pengisian bahan bakar, stasiun kereta api, dan di pinggiran jalan. Usaha bisnis ini mengalami pertumbuhan yang sangat pesat. Berdasarkan hasil wawancara dari Katadata.co.id dengan Ketua Umum Asosiasi Perusahaan Retail Indonesia (Aprindo). Pertumbuhan minimarket lebih dari 15 persen per tahunnya. Berdasarkan data tersebut para pemilik bisnis ini membutuhkan strategi untuk dapat bersaing dengan kompetitornya. Oleh karena itu diperlukan berbagai ma
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Leece, Michael, and Arnav Jhala. "Sequential Pattern Mining in StarCraft: Brood War for Short and Long-Term Goals." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 10, no. 2 (2021): 8–13. http://dx.doi.org/10.1609/aiide.v10i2.12736.

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A wide variety of strategies have been used to create agents in the growing field of real-time strategy AI. However, a frequent problem is the necessity of hand-crafting competencies, which becomes prohibitively difficult in a large space with many corner cases. A preferable approach would be to learn these competencies from the wealth of expert play available. We present a system that uses the Generalized Sequential Pattern (GSP) algorithm from data mining to find common patterns in StarCraft:Brood War replays at both the micro- and macro-level, and verify that these correspond to human under
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Puertas, Eloi, Sergio Escalera, and Oriol Pujol. "Generalized multi-scale stacked sequential learning for multi-class classification." Pattern Analysis and Applications 18, no. 2 (2013): 247–61. http://dx.doi.org/10.1007/s10044-013-0333-y.

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Kumar, Anil, Sonal Chawla, and Supreet Kaur Mann. "Sequential Pattern Mining and Hybrid Sentiment-based Collaborative Architecture for Rating Prediction." International Journal of Mathematical, Engineering and Management Sciences 10, no. 1 (2025): 148–62. https://doi.org/10.33889/ijmems.2025.10.1.009.

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This research presents a novel approach named “Sequential Pattern Mining and Hybrid Sentiment-based Collaborative Architecture for Rating Predictions". This approach overcomes the limitations of traditional techniques by considering multi-dimensional data, including users' past behaviour, buying patterns, and sentiments to enhance the rating predictions and recommendations. The proposed prediction approach incorporates users’ past behaviour i.e. ratings through the Collaborative Filtering technique. The users’ sentiments are included by implementing Hybrid Sentiment Analysis and the sequential
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Ou-Yang, Chao, Shih-Chung Chou, Yeh-Chun Juan, and Han-Cheng Wang. "Mining Sequential Patterns of Diseases Contracted and Medications Prescribed before the Development of Stevens-Johnson Syndrome in Taiwan." Applied Sciences 9, no. 12 (2019): 2434. http://dx.doi.org/10.3390/app9122434.

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Medication is designed to cure diseases, but serious risks can arise from severe adverse drug reactions (ADRs). ADRs can lead to emergency room visits and hospitalization, straining healthcare resources and, thus, they have strong implications for public health. Stevens–Johnson Syndrome (SJS) is one ADR and comprises the highest proportion of all drug relief cases in Taiwan. Pharmacovigilance involves the collection, detection, assessment, monitoring, and prevention of ADRs, including SJS. Most medical specialists are not fully aware of the risk of drug-induced SJS. Consequently, various drugs
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Dissertations / Theses on the topic "Generalized sequential pattern"

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Puertas, i. Prats Eloi. "Generalized Stacked Sequential Learning." Doctoral thesis, Universitat de Barcelona, 2014. http://hdl.handle.net/10803/285969.

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Over the past few decades, machine learning (ML) algorithms have become a very useful tool in tasks where designing and programming explicit, rule-based algorithms are infeasible. Some examples of applications where machine learning has been applied successfully are spam filtering, optical character recognition (OCR), search engines and computer vision. One of the most common tasks in ML is supervised learning, where the goal is to learn a general model able to predict the correct label of unseen examples from a set of known labeled input data. In supervised learning
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Book chapters on the topic "Generalized sequential pattern"

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Bureva, Veselina, Evdokia Sotirova, and Panagiotis Chountas. "Generalized Net of the Process of Sequential Pattern Mining by Generalized Sequential Pattern Algorithm (GSP)." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11310-4_72.

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Lin, Ming-Yen, Suh-Yin Lee, and Sheng-Shun Wang. "DELISP: Efficient Discovery of Generalized Sequential Patterns by Delimited Pattern-Growth Technology." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47887-6_19.

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Kryszkiewicz, Marzena, and Łukasz Skonieczny. "Fast Discovery of Generalized Sequential Patterns." In Studies in Big Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77604-0_12.

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Rasheeda Shameem, M., M. Razia Naseem, N. K. Subanivedhi, and R. Sethukkarasi. "A Dynamic Approach for Mining Generalised Sequential Patterns in Time Series Clinical Data Sets." In Advances in Computing and Information Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31552-7_67.

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Hsu, Wynne, Mong Li Lee, and Junmei Wang. "Mining Generalized Flow Patterns." In Temporal and Spatio-Temporal Data Mining. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-387-6.ch009.

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In this chapter, we investigate an efficient method to discover this class of relative-location sensitive flow patterns. These generalized flow patterns aim to summarize the sequential relationships between events that are prevalent in sharing the same topological structures. We adopt the pattern growth approach and develop an algorithm called GenSTMiner to discover these patterns. In order to increase the efficiency of the mining process, we also present two optimization techniques. The first is the use of conditional projected databases to prune infeasible events and sequences, and the second is pseudo projection to reduce memory requirement.
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Hankin, David G., Michael S. Mohr, and Ken B. Newman. "Spatially balanced sampling." In Sampling Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.003.0012.

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In many ecological and natural resource settings, there may be a high degree of spatial structure or pattern to the distribution of target variable values across the landscape. For example, the number of trees per hectare killed by a bark beetle infestation may be exceptionally high in one region of a national forest and near zero elsewhere. In such circumstances it may be highly desirable or even required that a sample survey directed at estimation of total tree mortality across a forest be based on selection of random locations that have good spatial balance, i.e., locations are well spread over the landscape with relatively even distances between them. A simple random sample cannot guarantee good spatial balance. We present two methods that have been proposed for selection of spatially balanced samples: GRTS (Generalized Random Tessellation Stratified Sampling) and BAS (Balanced Acceptance Sampling). Selection of samples using the GRTS approach involves a complicated series of sequential steps that allows generation of spatially balanced samples selected from finite populations or from infinite study areas. Selection of samples using BAS relies on the Halton sequence, is conceptually simpler, and produces samples that generally have better spatial balance than those produced by GRTS. Both approaches rely on use of software that is available in the R statistical/programming environment. Estimation relies on the Horvitz–Thompson estimator. Illustrative examples of running the SPSURVEY software package (used for GRTS) and links to the SDraw package (used for BAS) are provided at http://global.oup.com/uk/companion/hankin.
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Zhang, K., and D. Shasha. "Tree Pattern Matching." In Pattern Matching Algorithms. Oxford University Press, 1997. http://dx.doi.org/10.1093/oso/9780195113679.003.0014.

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Most of this book is about stringology, the study of strings. So why this chapter on trees? Why not graphs or geometry or something else? First, trees generalize strings in a very direct sense: a string is simply a tree with a single leaf. This has the unsurprising consequence that many of our algorithms specialize to strings and the happy consequence that some of those algorithms are as efficient as the best string algorithms. From the point of view of “treeology”, there is the additional pragmatic advantage of this relationship between trees and strings: some techniques from strings carry over to trees, e.g., suffix trees, and others show promise though we don’t know of work that exploits it. So, treeology provides a good example area for applications of stringologic techniques. Second, some of our friends in stringology may wonder whether there is some easy reduction that can take any tree edit problem, map it to strings, solve it in the string domain and then map it back. We don’t believe there is, because, as you will see, tree editing seems inherently to have more data dependence than string editing. (Specifically, the dynamic programming approach to string editing is always a local operation depending on the left, upper, and upper left neighbor of a cell. In tree editing, the upper left neighbor is usually irrelevant — instead the relevant cell depends on the tree topology.) That is a belief not a theorem, so we would like to state right at the outset the key open problem of treeology: can all tree edit problems on ordered trees (trees where the order among the siblings matters) be reduced efficiently to string edit problems and back again?. The rest of this chapter proceeds on the assumption that this question has a negative response. In particular, we discuss the best known algorithms for tree editing and several variations having to do with subtree removal, variable length don’t cares, and alignment. We discuss both sequential and parallel algorithms.
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Massari, Giovanni F., and Ilaria Giannoccaro. "Supply Chain Management." In The Oxford Handbook of Agent-based Computational Management Science. Oxford University Press, 2024. http://dx.doi.org/10.1093/oxfordhb/9780197668122.013.18.

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Abstract Supply chain conceptualizations have been oversimplified through linear systems made up of sequential dyadic relationships between vertically specialized firms. This view fails to capture the complex patterns of interdependencies that instead characterize modern supply chains, preventing managers from properly understanding the underlying structure of their supply chains and the design of the most effective coordination mechanisms. We investigate this issue through the theoretical lens of complex adaptive systems. The supply chain is conceptualized as a complex adaptive ecosystem where autonomous firms belonging to value chains originally separated coevolve over time by changing their individual behaviours while carrying out interdependent activities and interacting with each other, fostered by common goals. The archetypes of linear chains, networks, intertwined networks, closed-loop chains, and circular chains are conceptualized by means of different patterns of interdependencies, defined by the number and distribution of interdependent activities within the system. The presence of interdependencies limits the decision-making autonomy of the single firms and requires them working together through collaborative relationships. The latter in fact permit the mutual exchange of information for common benefits. A simple, yet insightful, agent-based model is then developed to investigate the effectiveness of three information-sharing mechanisms: those developing through vertical, horizontal, and lateral collaborative relationships, across the different supply chain structures. The generalized NKC fitness framework is adopted to model the complex patterns of interdependencies. The results of simulations permit us to draw theoretical and practical contributions.
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Conference papers on the topic "Generalized sequential pattern"

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Bicego, M., D. Gonzalez-Jimenez, E. Grosso, and J. L. Alba Castro. "Generalized Gaussian distributions for sequential data classification." In ICPR 2008 19th International Conference on Pattern Recognition. IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761771.

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Lu, Haitao, and Shuo Li. "A Generalized Weighted Closed Sequential Pattern Mining Algorithm with Item Interval." In 2015 International Conference on Automation, Mechanical Control and Computational Engineering. Atlantis Press, 2015. http://dx.doi.org/10.2991/amcce-15.2015.286.

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Krishna, Bandi, Chilukoori Ravinder, Rakesh Nayak, and Fasiahmad Parvez. "Hybrid Recommendation System with Enhanced Generalized Sequential Pattern Algorithm for ELearning System." In 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON). IEEE, 2023. http://dx.doi.org/10.1109/otcon56053.2023.10114040.

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Zheng, Jianping, Juan Tao, Jinfang Dou, and Baoming Bai. "Pattern-Aware Sequential Monte Carlo Detection for Generalized Space-Time Shift Keying." In 2014 IEEE International Conference on Computer and Information Technology (CIT). IEEE, 2014. http://dx.doi.org/10.1109/cit.2014.59.

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Destrilia, Rifkie PRIMARTHA, Sukemi, and Adi WIJAYA. "Online Retail Marketing Recommendation System Based on Generalized Sequential Pattern Algorithm and FP-Growth Algorithm." In Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019). Atlantis Press, 2020. http://dx.doi.org/10.2991/aisr.k.200424.053.

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Masseglia, F., P. Poncelet, and M. Teisseire. "Pre-processing time constraints for efficiently mining generalized sequential patterns." In Proceedings. 11th International Symposium on Temporal Representation and Reasoning, 2004. TIME 2004. IEEE, 2004. http://dx.doi.org/10.1109/time.2004.1314424.

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Cüppers, Joscha, and Jilles Vreeken. "Below the Surface: Summarizing Event Sequences with Generalized Sequential Patterns." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2023. http://dx.doi.org/10.1145/3580305.3599264.

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Dominique, S., and J. Y. Tre´panier. "Optimization of a Gas Turbine Engine Rotor Disc Using Case-Based Reasoning and the GATE Genetic Algorithm." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-23011.

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The implementation of an automated decision support system in the field of structural design and optimization can give a significant advantage to any industry working on mechanical design. Such a system can reduce the project cycle time or allow more time to produce a better design by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work. This paper presents an approach to automating the process of designing a gas turbine engine rotor disc using case-based reasoning (CBR), combined with a new genetic algorithm, the Genetic Algorithm
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Kumar, Ashutosh. "Transformer-Based Deep Learning Models for Well Log Processing and Quality Control by Modelling Global Dependence of the Complex Sequences." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/208109-ms.

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Abstract A single well from any mature field produces approximately 1.7 million Measurement While Drilling (MWD) data points. We either use cross-correlation and covariance measurement, or Long Short-Term Memory (LSTM) based Deep Learning algorithms to diagnose long sequences of extremely noisy data. LSTM's context size of 200 tokens barely accounts for the entire depth. Proposed work develops application of Transformer-based Deep Learning algorithm to diagnose and predict events in complex sequences of well-log data. Sequential models learn geological patterns and petrophysical trends to dete
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