Готові списки джерел за темами / Advanced Pattern Mining

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Опубліковано: 27 липня 2024
Оновлено: 28 липня 2024

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Статті в журналах з теми "Advanced Pattern Mining"

1

Sharma, Vikrant. "Relevance Feature Discovery for Text Mining." Mathematical Statistician and Engineering Applications 70, no. 1 (January 31, 2021): 225–33. http://dx.doi.org/10.17762/msea.v70i1.2303.

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Due to large size words also data patterns, it is difficult to ensure the quality of relevant characteristics that are found in text documents that describe user preferences. Most widely used text mining and classification techniques now in use have embraced term-based strategies. However, polysemy and synonymy issues have affected them all. The theory that pattern-based approaches should outperform term-based ones in performance in expressing user preferences has been often held throughout the years, however text mining still struggles with how to employ large-scale patterns successfully. This research introduces a novel methodology for relevance feature discovery to address this hard problem. It finds higher level features in text texts that are both positive and negative patterns and uses them instead of low-level features (terms). Additionally, it organised terms into categories and updates term weights according to the patterns and specificity of those distributions. Significant tests employing this model on the datasets RCV1, TREC themes, and Reuters-21578 reveal that it performs noticeably better than both the most advanced term-based approaches and pattern-based methods.
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2

Santoro, Diego, Andrea Tonon, and Fabio Vandin. "Mining Sequential Patterns with VC-Dimension and Rademacher Complexity." Algorithms 13, no. 5 (May 18, 2020): 123. http://dx.doi.org/10.3390/a13050123.

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Sequential pattern mining is a fundamental data mining task with application in several domains. We study two variants of this task—the first is the extraction of frequent sequential patterns, whose frequency in a dataset of sequential transactions is higher than a user-provided threshold; the second is the mining of true frequent sequential patterns, which appear with probability above a user-defined threshold in transactions drawn from the generative process underlying the data. We present the first sampling-based algorithm to mine, with high confidence, a rigorous approximation of the frequent sequential patterns from massive datasets. We also present the first algorithms to mine approximations of the true frequent sequential patterns with rigorous guarantees on the quality of the output. Our algorithms are based on novel applications of Vapnik-Chervonenkis dimension and Rademacher complexity, advanced tools from statistical learning theory, to sequential pattern mining. Our extensive experimental evaluation shows that our algorithms provide high-quality approximations for both problems we consider.
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3

Ti, Zhengyi, Jiazhen Li, Meng Wang, Kang Wang, Zhupeng Jin, and Caiwang Tai. "Fracture Mechanism in Overlying Strata during Longwall Mining." Shock and Vibration 2021 (June 21, 2021): 1–15. http://dx.doi.org/10.1155/2021/4764732.

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We used the key stratum theory to establish a more realistic thin-plate mechanical model of elastic foundation clamped boundary and study the fracture mechanism of overlying strata during longwall mining. We analyzed the fracture characteristics and factors affecting fracture of the key stratum combined with the Mohr–Coulomb yield criterion. Besides, we used numerical simulation methods to verify the evolution pattern of the overlying strata fracture. The results show that the fracture mechanisms of the elastic foundation clamped structure’s key stratum varied depending on the position under longwall mining. The advanced coal wall area of the upper surface is a compressive-shear fracture. The center area of the lower surface is a tensile fracture. With the increase of the excavation length and the load of the key stratum, the central area and the advanced coal wall area of the long side are fractured before the advanced coal wall area of the short side. With the increase of flexural rigidity of the key stratum, the advanced coal wall area of the long side fractures before the central area and the advanced coal wall area of the short side. With the increase of the foundation modulus and the advanced load of the key stratum, the central area fractures before the surrounding advanced coal wall area. The advanced influence distance was positively correlated with the key stratum’s flexural rigidity and advanced load and negatively correlated with the foundation modulus and excavation length. The advanced influence distance was not affected by the load of the key stratum. The numerical simulation results show that, with the increase of the mining area, the fracture trace of overlying strata in goaf extended to the coal wall’s interior. The fracture range of overlying strata is larger than that of the miningd: area. This study has a practical value for water disasters, gas outbursts, and rock strata control.
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Selmaoui-Folcher, Nazha, Frédéric Flouvat, Dominique Gay, and Isabelle Rouet. "Spatial Pattern Mining for Soil Erosion Characterization." International Journal of Agricultural and Environmental Information Systems 2, no. 2 (July 2011): 73–92. http://dx.doi.org/10.4018/jaeis.2011070105.

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The protection and the maintenance of the exceptional environment of New Caledonia are major goals for this territory. Among environmental problems, erosion has a strong impact on terrestrial and coastal ecosystems. However, due to the volume of data and its complexity, assessment of hazard at a regional scale is time-consuming, costly and rarely updated. Therefore, understanding and predicting environmental phenomenons need advanced techniques of analysis and modelization. In order to improve the understanding of the erosion phenomenon, this paper proposes a spatial approach based on co-location mining and GIS. Considering a set of Boolean spatial features, the goal of co-location mining is to find subsets of features often located together. This system provides useful and interpretable knowledge based on a new interestingness measure for co-locations and a new visualization of the discovered knowledge. The interestingness measure better reflects the importance of a co-location for the experts, and is completely integrated in the mining process. The visualization approach is a simple, concise and intuitive representation of the co-locations that takes into consideration the spatial nature of the underlying objects and the experts practice.
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5

Khoshahval, S., M. Farnaghi, and M. Taleai. "SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 395–99. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-395-2017.

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Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.
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6

Patel, Pratik C., and Upasna Singh. "A novel classification model for data theft detection using advanced pattern mining." Digital Investigation 10, no. 4 (December 2013): 385–97. http://dx.doi.org/10.1016/j.diin.2013.09.002.

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7

Hsu, Kuo-Wei. "Efficiently and Effectively Mining Time-Constrained Sequential Patterns of Smartphone Application Usage." Mobile Information Systems 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/3689309.

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Today, we have the freedom to install and use all kinds of applications on smartphones, thanks to the development of mobile communication and computing technologies. Undoubtedly, the system and application developers are eager to know how we use the applications on our smartphones in our daily life and so are the researchers. In this paper, we present our work on developing a pattern mining algorithm and applying it to smartphone application usage log collected from tens of smartphone users for several years. Our goal is to mine the sequential patterns each of which presents a series of application uses and satisfies a constraint on the maximum time interval between two application uses. However, we cannot mine such patterns by general algorithms and will miss some patterns by using the widely used implementation of the advanced algorithm specifically designed for time-constrained sequential pattern mining. We not only present an algorithm that can efficiently and effectively mine the patterns in which we are interested but also discuss and visualize the mined patterns. Our work could potentially support the related studies.
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8

Kim, Hyeonmo, Heonho Kim, Sinyoung Kim, Hanju Kim, Myungha Cho, Bay Vo, Jerry Chun-Wei Lin, and Unil Yun. "An advanced approach for incremental flexible periodic pattern mining on time-series data." Expert Systems with Applications 230 (November 2023): 120697. http://dx.doi.org/10.1016/j.eswa.2023.120697.

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Liu, Xiaodong, Shuming Zhang, Weiwen Cui, Hong Zhang, Rui Wu, Jie Huang, Zhixin Li, Xiaohan Wang, Jianing Wu, and Junqi Yang. "A Workflow Investigating the Information behind the Time-Series Energy Consumption Condition via Data Mining." Buildings 13, no. 9 (September 10, 2023): 2303. http://dx.doi.org/10.3390/buildings13092303.

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The purpose of this study is to develop a framework to understand building energy usage pattern finding using data mining algorithms. Developing advanced techniques and requirements for carbon emission reduction provides higher demands for building energy efficiency. Research conducted so far has mainly focused on total energy consumption data clusters instead of time-series curve peculiarity. This research adopts the time-series cluster algorithm k-shape and the ARM Apriori method to study the simulation database generated by the official restaurant energy model. These advanced data mining techniques can discover potential information hidden in a big database that has not been identified by people. The results show that the restaurant time-series energy consumption curve can be clustered into four type patterns: Invert U, M, Invert V, and Multiple M. Each mode has its own variation characteristics. Two aspects for the solution of intensity and peak shift are proposed, achieving energy savings and focusing on different curve modes. The conclusion shows that the combination of time-series clustering and the ARM algorithm work flow can successfully discover the building operation pattern. Some solutions focusing on restaurant energy usage issues have been proposed, and future investigations should pay more attention to building area-influenced factors.
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Shen, Hai Ying, Shu Ming Wen, and Ti Zhuan Wang. "Mining Engineering Professionals Motivated to Improve their Proficiency in English." Applied Mechanics and Materials 525 (February 2014): 765–69. http://dx.doi.org/10.4028/www.scientific.net/amm.525.765.

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The industrialization is progressing on the unparalleled scale, bringing an ever greater impetus to the growth of mining industry. China has had its unique pattern of developing its mining engineerin: firstly importing technologies, then studying and absorbing them, and finally achieving some innovation. Until now, China has been honored with some of mining theories taking the lead in the international mining world. As the disseminators of human civilization including advanced mining technologies, the Chinese professionals concerned are duty-bound to introduce abroad Chinas new progresses in mining engineering; that they are skillfully equipped with the four kinds of English abilities in reading, writing, listening and speaking, which is prerequisite for them to work well in foreign cooperation, has become an urgent matter to for them as well as for the authorities concerned.
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Дисертації з теми "Advanced Pattern Mining"

1

Verzotto, Davide. "Advanced Computational Methods for Massive Biological Sequence Analysis." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3426282.

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With the advent of modern sequencing technologies massive amounts of biological data, from protein sequences to entire genomes, are becoming increasingly available. This poses the need for the automatic analysis and classification of such a huge collection of data, in order to enhance knowledge in the Life Sciences. Although many research efforts have been made to mathematically model this information, for example finding patterns and similarities among protein or genome sequences, these approaches often lack structures that address specific biological issues. In this thesis, we present novel computational methods for three fundamental problems in molecular biology: the detection of remote evolutionary relationships among protein sequences, the identification of subtle biological signals in related genome or protein functional sites, and the phylogeny reconstruction by means of whole-genome comparisons. The main contribution is given by a systematic analysis of patterns that may affect these tasks, leading to the design of practical and efficient new pattern discovery tools. We thus introduce two advanced paradigms of pattern discovery and filtering based on the insight that functional and conserved biological motifs, or patterns, should lie in different sites of sequences. This enables to carry out space-conscious approaches that avoid a multiple counting of the same patterns. The first paradigm considered, namely irredundant common motifs, concerns the discovery of common patterns, for two sequences, that have occurrences not covered by other patterns, whose coverage is defined by means of specificity and extension. The second paradigm, namely underlying motifs, concerns the filtering of patterns, from a given set, that have occurrences not overlapping other patterns with higher priority, where priority is defined by lexicographic properties of patterns on the boundary between pattern matching and statistical analysis. We develop three practical methods directly based on these advanced paradigms. Experimental results indicate that we are able to identify subtle similarities among biological sequences, using the same type of information only once. In particular, we employ the irredundant common motifs and the statistics based on these patterns to solve the remote protein homology detection problem. Results show that our approach, called Irredundant Class, outperforms the state-of-the-art methods in a challenging benchmark for protein analysis. Afterwards, we establish how to compare and filter a large number of complex motifs (e.g., degenerate motifs) obtained from modern motif discovery tools, in order to identify subtle signals in different biological contexts. In this case we employ the notion of underlying motifs. Tests on large protein families indicate that we drastically reduce the number of motifs that scientists should manually inspect, further highlighting the actual functional motifs. Finally, we combine the two proposed paradigms to allow the comparison of whole genomes, and thus the construction of a novel and practical distance function. With our method, called Unic Subword Approach, we relate to each other the regions of two genome sequences by selecting conserved motifs during evolution. Experimental results show that our approach achieves better performance than other state-of-the-art methods in the whole-genome phylogeny reconstruction of viruses, prokaryotes, and unicellular eukaryotes, further identifying the major clades of these organisms.
Con l'avvento delle moderne tecnologie di sequenziamento, massive quantità di dati biologici, da sequenze proteiche fino a interi genomi, sono disponibili per la ricerca. Questo progresso richiede l'analisi e la classificazione automatica di tali collezioni di dati, al fine di migliorare la conoscenza nel campo delle Scienze della Vita. Nonostante finora siano stati proposti molti approcci per modellare matematicamente le sequenze biologiche, ad esempio cercando pattern e similarità tra sequenze genomiche o proteiche, questi metodi spesso mancano di strutture in grado di indirizzare specifiche questioni biologiche. In questa tesi, presentiamo nuovi metodi computazionali per tre problemi fondamentali della biologia molecolare: la scoperta di relazioni evolutive remote tra sequenze proteiche, l'individuazione di segnali biologici complessi in siti funzionali tra loro correlati, e la ricostruzione della filogenesi di un insieme di organismi, attraverso la comparazione di interi genomi. Il principale contributo è dato dall'analisi sistematica dei pattern che possono interessare questi problemi, portando alla progettazione di nuovi strumenti computazionali efficaci ed efficienti. Vengono introdotti così due paradigmi avanzati per la scoperta e il filtraggio di pattern, basati sull'osservazione che i motivi biologici funzionali, o pattern, sono localizzati in differenti regioni delle sequenze in esame. Questa osservazione consente di realizzare approcci parsimoniosi in grado di evitare un conteggio multiplo degli stessi pattern. Il primo paradigma considerato, ovvero irredundant common motifs, riguarda la scoperta di pattern comuni a coppie di sequenze che hanno occorrenze non coperte da altri pattern, la cui copertura è definita da una maggiore specificità e/o possibile estensione dei pattern. Il secondo paradigma, ovvero underlying motifs, riguarda il filtraggio di pattern che hanno occorrenze non sovrapposte a quelle di altri pattern con maggiore priorità, dove la priorità è definita da proprietà lessicografiche dei pattern al confine tra pattern matching e analisi statistica. Sono stati sviluppati tre metodi computazionali basati su questi paradigmi avanzati. I risultati sperimentali indicano che i nostri metodi sono in grado di identificare le principali similitudini tra sequenze biologiche, utilizzando l'informazione presente in maniera non ridondante. In particolare, impiegando gli irredundant common motifs e le statistiche basate su questi pattern risolviamo il problema della rilevazione di omologie remote tra proteine. I risultati evidenziano che il nostro approccio, chiamato Irredundant Class, ottiene ottime prestazioni su un benchmark impegnativo, e migliora i metodi allo stato dell'arte. Inoltre, per individuare segnali biologici complessi utilizziamo la nozione di underlying motifs, definendo così alcune modalità per il confronto e il filtraggio di motivi degenerati ottenuti tramite moderni strumenti di pattern discovery. Esperimenti su grandi famiglie proteiche dimostrano che il nostro metodo riduce drasticamente il numero di motivi che gli scienziati dovrebbero altrimenti ispezionare manualmente, mettendo in luce inoltre i motivi funzionali identificati in letteratura. Infine, combinando i due paradigmi proposti presentiamo una nuova e pratica funzione di distanza tra interi genomi. Con il nostro metodo, chiamato Unic Subword Approach, relazioniamo tra loro le diverse regioni di due sequenze genomiche, selezionando i motivi conservati durante l'evoluzione. I risultati sperimentali evidenziano che il nostro approccio offre migliori prestazioni rispetto ad altri metodi allo stato dell'arte nella ricostruzione della filogenesi di organismi quali virus, procarioti ed eucarioti unicellulari, identificando inoltre le sottoclassi principali di queste specie.
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2

Kanodia, Juveria. "Structural advances for pattern discovery in multi-relational databases /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/978.

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Mehrabi, Saeed. "Advanced natural language processing and temporal mining for clinical discovery." 2015. http://hdl.handle.net/1805/8895.

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Indiana University-Purdue University Indianapolis (IUPUI)
There has been vast and growing amount of healthcare data especially with the rapid adoption of electronic health records (EHRs) as a result of the HITECH act of 2009. It is estimated that around 80% of the clinical information resides in the unstructured narrative of an EHR. Recently, natural language processing (NLP) techniques have offered opportunities to extract information from unstructured clinical texts needed for various clinical applications. A popular method for enabling secondary uses of EHRs is information or concept extraction, a subtask of NLP that seeks to locate and classify elements within text based on the context. Extraction of clinical concepts without considering the context has many complications, including inaccurate diagnosis of patients and contamination of study cohorts. Identifying the negation status and whether a clinical concept belongs to patients or his family members are two of the challenges faced in context detection. A negation algorithm called Dependency Parser Negation (DEEPEN) has been developed in this research study by taking into account the dependency relationship between negation words and concepts within a sentence using the Stanford Dependency Parser. The study results demonstrate that DEEPEN, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs. Additionally, an NLP system consisting of section segmentation and relation discovery was developed to identify patients' family history. To assess the generalizability of the negation and family history algorithm, data from a different clinical institution was used in both algorithm evaluations.
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Книги з теми "Advanced Pattern Mining"

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Huang, De-Shuang. Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques: 4th International Conference on Intelligent Computing, ICIC 2008 Shanghai, China, September 15-18, 2008 Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.

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2

International, Conference on Advances in Pattern Recognition (3rd 2005 Bath England). Third International Conference on Advances in Pattern Recognition: ICAPR 2005, Bath, UK, August 22-25, 2005 : proceedings. Berlin: Springer, 2005.

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3

Singh, Sameer. International Conference on Advances in Pattern Recognition: Proceedings of ICAPR '98, 23-25 November 1998, Plymouth, UK. London: Springer London, 1999.

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4

Mexican Conference on Pattern Recognition (2nd 2010 Puebla, Mexico). Advances in pattern recognition: Second Mexican Conference on Pattern Recognition, MCPR 2010, Puebla, Mexico, September 27-29, 2010 : proceedings. Berlin: Springer, 2010.

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5

International Workshop on Graphics Recognition (7th 2007 Barcelona, Spain). Graphics recognition: Recent advances and new opportunities : 7th international workshop, GREC 2007, Curitiba, Brazil, September 20-21, 2007 : selected papers. [New York]: Springer, 2008.

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6

International Workshop on Graphics Recognition (7th 2007 Barcelona, Spain). Graphics recognition: Recent advances and new opportunities : 7th international workshop, GREC 2007, Curitiba, Brazil, September 20-21, 2007 : selected papers. [New York]: Springer, 2008.

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7

David, Hutchison. Advances in Artificial Intelligence: 22nd Canadian Conference on Artificial Intelligence, Canadian AI 2009 Kelowna, Canada, May 25-27, 2009 Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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8

Takashi, Washio, and SpringerLink (Online service), eds. Advances in Machine Learning: First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2-4, 2009. Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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9

Sidorov, Grigori. Advances in Artificial Intelligence: 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Pachuca, Mexico, November 8-13, 2010, Proceedings, Part I. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.

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10

Li, Shipeng. Advances in Multimedia Modeling: 19th International Conference, MMM 2013, Huangshan, China, January 7-9, 2013, Proceedings, Part I. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Частини книг з теми "Advanced Pattern Mining"

1

Aggarwal, Charu C. "Association Pattern Mining: Advanced Concepts." In Data Mining, 135–52. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_5.

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Eavis, Todd, and Xi Zheng. "Multi-level Frequent Pattern Mining." In Database Systems for Advanced Applications, 369–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00887-0_33.

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Pears, Russel, Yun Sing Koh, and Gillian Dobbie. "Discriminatory Confidence Analysis in Pattern Mining." In Advanced Data Mining and Applications, 285–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25853-4_22.

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Koper, Adam, and Hung Son Nguyen. "Sequential Pattern Mining from Stream Data." In Advanced Data Mining and Applications, 278–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25856-5_21.

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Nohuddin, Puteri N. E., Rob Christley, Frans Coenen, Yogesh Patel, Christian Setzkorn, and Shane Williams. "Frequent Pattern Trend Analysis in Social Networks." In Advanced Data Mining and Applications, 358–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17316-5_35.

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Wang, Yida, Ee-Peng Lim, and San-Yih Hwang. "Efficient Group Pattern Mining Using Data Summarization." In Database Systems for Advanced Applications, 895–907. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24571-1_78.

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Sun, Liping, and Xiuzhen Zhang. "Efficient Frequent Pattern Mining on Web Logs." In Advanced Web Technologies and Applications, 533–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24655-8_58.

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Zhang, Xiaolong, Wenjuan Gong, and Yoshihiro Kawamura. "Customer Behavior Pattern Discovering with Web Mining." In Advanced Web Technologies and Applications, 844–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24655-8_92.

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Gu, Mi Sug, Jeong Hee Hwang, and Keun Ho Ryu. "Weigted-FP-Tree Based XML Query Pattern Mining." In Advanced Data Mining and Applications, 417–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17316-5_40.

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Muzammal, Muhammad, and Rajeev Raman. "On Probabilistic Models for Uncertain Sequential Pattern Mining." In Advanced Data Mining and Applications, 60–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17316-5_6.

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Тези доповідей конференцій з теми "Advanced Pattern Mining"

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Gaur, Deepti, Aditya Shastri, and Ranjit Biswas. "Metagraph-Based Substructure Pattern Mining." In 2008 International Conference on Advanced Computer Theory and Engineering (ICACTE). IEEE, 2008. http://dx.doi.org/10.1109/icacte.2008.100.

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Hazarika, Shyamanta M. "Pattern Mining as Abduction from Snapshots to Spatio-temporal Patterns." In 15th International Conference on Advanced Computing and Communications (ADCOM 2007). IEEE, 2007. http://dx.doi.org/10.1109/adcom.2007.59.

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Xiaoliang Geng, H. Arimura, and T. Uno. "Pattern Mining from Trajectory GPS Data." In 2012 IIAI International Conference on Advanced Applied Informatics (IIAIAAI 2012). IEEE, 2012. http://dx.doi.org/10.1109/iiai-aai.2012.21.

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Zhou, Cheng, Boris Cule, and Bart Goethals. "Cohesion based co-location pattern mining." In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2015. http://dx.doi.org/10.1109/dsaa.2015.7344839.

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Kadimisetty, Avinash, C. Oswald, and B. Sivaselvan. "Frequent Pattern Mining Approach to Image Compression." In 2016 22nd Annual International Conference on Advanced Computing and Communication (ADCOM). IEEE, 2016. http://dx.doi.org/10.1109/adcom.2016.14.

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Teoh, Edward, Vito Dai, Luigi Capodieci, Ya-Chieh Lai, and Frank Gennari. "Systematic data mining using a pattern database to accelerate yield ramp." In SPIE Advanced Lithography, edited by John L. Sturtevant and Luigi Capodieci. SPIE, 2014. http://dx.doi.org/10.1117/12.2047307.

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Gmati, Chekib, Oumaima Sliti, Habib Hamam, and Zied Lachiri. "Frequent pattern mining for online handwriting recognition." In 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). IEEE, 2018. http://dx.doi.org/10.1109/atsip.2018.8364482.

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Zhou, Mingming, and Yabo Xu. "Injecting Pedagogical Constraints into Sequential Learning Pattern Mining." In 2010 IEEE 10th International Conference on Advanced Learning Technologies (ICALT). IEEE, 2010. http://dx.doi.org/10.1109/icalt.2010.108.

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Sjöman, Heikki, and Martin Steinert. "Applying Sequential Pattern Mining to Portable RFID System Data." In 6th International Workshop of Advanced Manufacturing and Automation. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/iwama-16.2016.5.

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Trivonanda, Ridho, Rahmad Mahendra, Indra Budi, and Rani Aulia Hidayat. "Sequential Pattern Mining for e-Commerce Recommender System." In 2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE, 2020. http://dx.doi.org/10.1109/icacsis51025.2020.9263192.

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