Littérature scientifique sur le sujet « Approximate Mining »
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Articles de revues sur le sujet "Approximate Mining"
Livshits, Ester, Alireza Heidari, Ihab F. Ilyas et Benny Kimelfeld. « Approximate denial constraints ». Proceedings of the VLDB Endowment 13, no 10 (juin 2020) : 1682–95. http://dx.doi.org/10.14778/3401960.3401966.
Texte intégralYip, Kelly K., et David A. Nembhard. « Mining approximate sequential patterns with gaps ». International Journal of Data Mining, Modelling and Management 7, no 2 (2015) : 108. http://dx.doi.org/10.1504/ijdmmm.2015.069249.
Texte intégralCombi, Carlo, et Pietro Sala. « Mining approximate interval-based temporal dependencies ». Acta Informatica 53, no 6-8 (14 septembre 2015) : 547–85. http://dx.doi.org/10.1007/s00236-015-0246-x.
Texte intégralChen, Yan, et Aijun An. « Approximate Parallel High Utility Itemset Mining ». Big Data Research 6 (décembre 2016) : 26–42. http://dx.doi.org/10.1016/j.bdr.2016.07.001.
Texte intégralSu, Na, Zhe Hui Wu, Ji Min Liu, Tai An Liu, Xin Jun An et Chang Qing Yan. « Mining Approximate Frequent Itemsets over Data Streams ». Applied Mechanics and Materials 685 (octobre 2014) : 536–39. http://dx.doi.org/10.4028/www.scientific.net/amm.685.536.
Texte intégralSilvestri, Claudio, et Salvatore Orlando. « Approximate mining of frequent patterns on streams ». Intelligent Data Analysis 11, no 1 (15 mars 2007) : 49–73. http://dx.doi.org/10.3233/ida-2007-11104.
Texte intégralMcCoy, Corren G., Michael L. Nelson et Michele C. Weigle. « Mining the Web to approximate university rankings ». Information Discovery and Delivery 46, no 3 (20 août 2018) : 173–83. http://dx.doi.org/10.1108/idd-05-2018-0014.
Texte intégralMazlack, Lawrence J. « Approximate reasoning applied to unsupervised database mining ». International Journal of Intelligent Systems 12, no 5 (mai 1997) : 391–414. http://dx.doi.org/10.1002/(sici)1098-111x(199705)12:5<391 ::aid-int3>3.0.co;2-i.
Texte intégralBashir, Shariq, et Daphne Teck Ching Lai. « Mining Approximate Frequent Itemsets Using Pattern Growth Approach ». Information Technology and Control 50, no 4 (16 décembre 2021) : 627–44. http://dx.doi.org/10.5755/j01.itc.50.4.29060.
Texte intégralCHEN, Siyu, Ning WANG et Mengmeng ZHANG. « Mining Approximate Primary Functional Dependency on Web Tables ». IEICE Transactions on Information and Systems E102.D, no 3 (1 mars 2019) : 650–54. http://dx.doi.org/10.1587/transinf.2018edl8130.
Texte intégralThèses sur le sujet "Approximate Mining"
Kolli, Lakshmi Priya. « Mining for Frequent Community Structures using Approximate Graph Matching ». University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623166375110273.
Texte intégralAgarwal, Khushbu. « A partition based approach to approximate tree mining a memory hierarchy perspective / ». Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1196284256.
Texte intégralAgarwal, Khushbu. « A partition based approach to approximate tree mining : a memory hierarchy perspective ». The Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1196284256.
Texte intégralCarraher, Lee A. « Approximate Clustering Algorithms for High Dimensional Streaming and Distributed Data ». University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511860805777818.
Texte intégralFuhry, David P. « PLASMA-HD : Probing the LAttice Structure and MAkeup of High-dimensional Data ». The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440431146.
Texte intégralSartipi, Kamran. « Software Architecture Recovery based on Pattern Matching ». Thesis, University of Waterloo, 2003. http://hdl.handle.net/10012/1122.
Texte intégralKuo, Fang-Chen, et 郭芳甄. « Mining Approximate Frequent Itemsets from Noisy Data ». Thesis, 2008. http://ndltd.ncl.edu.tw/handle/6waagh.
Texte intégral東吳大學
資訊科學系
96
To discover association rules, frequent itemset mining can find out items that appear frequently together in a dataset and to be the first step in the analysis of data arising in broad range of application. Moreover, industry can use mining result to improve marketing strategy and profitability. Traditional frequent itemset mining utilizes the “exact” mode. However, the exact-mode mining is not appropriate for real data. Mining noisy data using the exact mode cannot generate correct frequent itemsets, and may eventually lead to incorrect decisions. In recent years, many researchers have studied how to discover frequent itemsets from noisy data. However, existing methods can become inefficient when the dataset is sparse. Therefore, these methods cannot be applied to all kinds of datasets. In this paper, we propose a new algorithm, called the TAFI algorithm, for mining approximate frequent itemsets. The TAFI algorithm not only can correctly and efficiently discover approximate frequent itemsets from noisy data, but also can perform well with spare datasets.
Mantovani, Matteo. « Approximate Data Mining Techniques on Clinical Data ». Doctoral thesis, 2020. http://hdl.handle.net/11562/1018039.
Texte intégralLiu, Chunyang. « Summarizing data with representative patterns ». Thesis, 2016. http://hdl.handle.net/10453/52923.
Texte intégralThe advance of technology makes data acquisition and storage become unprecedentedly convenient. It contributes to the rapid growth of not only the volume but also the veracity and variety of data in recent years, which poses new challenges to the data mining area. For example, uncertain data mining emerges due to its capability to model the inherent veracity of data; spatial data mining attracts much research attention as the widespread of location-based services and wearable devices. As a fundamental topic of data mining, how to effectively and efficiently summarize data in this situation still remains to be explored. This thesis studied the problem of summarizing data with representative patterns. The objective is to find a set of patterns, which is much more concise but still contains rich information of the original data, and may provide valuable insights for further analysis of data. In the light of this idea, we formally formulate the problem and provide effective and efficient solutions in various scenarios. We study the problem of summarizing probabilistic frequent patterns over uncertain data. Probabilistic frequent pattern mining over uncertain data has received much research attention due to the wide applicabilities of uncertain data. It suffers from the problem of generating an exponential number of result patterns, which hinders the analysis of patterns and calls for the need to find a small number of representative patterns to approximate all other patterns. We formally formulate the problem of probabilistic representative frequent pattern (P-RFP) mining, which aims to find the minimal set of patterns with sufficiently high probability to represent all other patterns. The bottleneck turns out to be checking whether a pattern can probabilistically represent another, which involves the computation of a joint probability of the supports of two patterns. We propose a novel dynamic programming-based approach to address the problem and devise effective optimization strategies to improve the computation efficiency. To enhance the practicability of P-RFP mining, we introduce a novel approximation of the joint probability with both theoretical and empirical proofs. Based on the approximation, we propose an Approximate P-RFP Mining (APM) algorithm, which effectively and efficiently compresses the probabilistic frequent pattern set. The error rate of APM is guaranteed to be very small when the database contains hundreds of transactions, which further affirms that APM is a practical solution for summarizing probabilistic frequent patterns. We address the problem of directly summarizing uncertain transaction database by formulating the problem as Minimal Probabilistic Tile Cover Mining, which aims to find a high-quality probabilistic tile set covering an uncertain database with minimal cost. We define the concept of Probabilistic Price and Probabilistic Price Order to evaluate and compare the quality of tiles, and propose a framework to discover the minimal probabilistic tile cover. The bottleneck is to check whether a tile is better than another according to the Probabilistic Price Order, which involves the computation of a joint probability. We prove that it can be decomposed into independent terms and calculated efficiently. Several optimization techniques are devised to further improve the performance. We analyze the problem of summarizing co-locations mined from spatial databases. Co-location pattern mining finds patterns of spatial features whose instances tend to locate together in geographic space. However, the traditional framework of co-location pattern mining produces an exponential number of patterns because of the downward closure property, which makes it difficult for users to understand, assess or apply the huge number of resulted patterns. To address this issue, we study the problem of mining representative co-location patterns (RCP). We first define a covering relationship between two co-location patterns then formally formulate the problem of Representative Co-location Pattern mining. To solve the problem of RCP mining, we propose the RCPFast algorithm adopting the post-mining framework and the RCPMS algorithm pushing pattern summarization into the co-location mining process.
董原賓. « Approximately mining frequent representative itemsets on data streams ». Thesis, 2006. http://ndltd.ncl.edu.tw/handle/31159769915805928034.
Texte intégralLivres sur le sujet "Approximate Mining"
Henry G, Burnett, et Bret Louis-Alexis. Part I Host States, Mining Companies, and Mining Projects, 2 Mining Companies. Oxford University Press, 2017. http://dx.doi.org/10.1093/law/9780198757641.003.0002.
Texte intégralSobczyk, Eugeniusz Jacek. Uciążliwość eksploatacji złóż węgla kamiennego wynikająca z warunków geologicznych i górniczych. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN, 2022. http://dx.doi.org/10.33223/onermin/0222.
Texte intégralChapitres de livres sur le sujet "Approximate Mining"
Fiorentino, Nicola, Cristian Molinaro et Irina Trubitsyna. « Approximate Query Answering over Incomplete Data ». Dans Complex Pattern Mining, 213–27. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36617-9_13.
Texte intégralWang, Liang, Christopher Leckie, Kotagiri Ramamohanarao et James Bezdek. « Approximate Spectral Clustering ». Dans Advances in Knowledge Discovery and Data Mining, 134–46. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_15.
Texte intégralTripathy, B. K., Prateek Saraf et S. Ch Parida. « On Multigranular Approximate Rough Equivalence of Sets and Approximate Reasoning ». Dans Computational Intelligence in Data Mining - Volume 2, 605–16. New Delhi : Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2208-8_55.
Texte intégralFerreira, Pedro G., Paulo J. Azevedo, Cândida G. Silva et Rui M. M. Brito. « Mining Approximate Motifs in Time Series ». Dans Discovery Science, 89–101. Berlin, Heidelberg : Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893318_12.
Texte intégralSoulet, Arnaud, et François Rioult. « Exact and Approximate Minimal Pattern Mining ». Dans Advances in Knowledge Discovery and Management, 61–81. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45763-5_4.
Texte intégralAizaz, Zainab, Kavita Khare et Aizaz Tirmizi. « Efficient Approximate Multipliers for Neural Network Applications ». Dans Computational Intelligence in Data Mining, 577–89. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9447-9_44.
Texte intégralAcosta-Mendoza, Niusvel, Andrés Gago-Alonso et José E. Medina-Pagola. « On Speeding up Frequent Approximate Subgraph Mining ». Dans Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 316–23. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33275-3_39.
Texte intégralDing, Lizhong, et Shizhong Liao. « Nyström Approximate Model Selection for LSSVM ». Dans Advances in Knowledge Discovery and Data Mining, 282–93. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30217-6_24.
Texte intégralWang, Jingdong, Jing Wang, Qifa Ke, Gang Zeng et Shipeng Li. « Fast Approximate $$K$$ K -Means via Cluster Closures ». Dans Multimedia Data Mining and Analytics, 373–95. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_17.
Texte intégralZong, Yu, Guandong Xu, Ping Jin, Yanchun Zhang, EnHong Chen et Rong Pan. « APPECT : An Approximate Backbone-Based Clustering Algorithm for Tags ». Dans Advanced Data Mining and Applications, 175–89. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25853-4_14.
Texte intégralActes de conférences sur le sujet "Approximate Mining"
Vilim, Matthew, Henry Duwe et Rakesh Kumar. « Approximate bitcoin mining ». Dans DAC '16 : The 53rd Annual Design Automation Conference 2016. New York, NY, USA : ACM, 2016. http://dx.doi.org/10.1145/2897937.2897988.
Texte intégralSpyropoulou, Eirini, et Tijl De Bie. « Mining approximate multi-relational patterns ». Dans 2014 International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2014. http://dx.doi.org/10.1109/dsaa.2014.7058115.
Texte intégralNing Pan, Zhiqiang Zhu, Liangsheng He, Lei Sun et Hang Su. « Mining approximate roles under important assignment ». Dans 2016 2nd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2016. http://dx.doi.org/10.1109/compcomm.2016.7924918.
Texte intégralLi, Ruirui, et Wei Wang. « REAFUM : Representative Approximate Frequent Subgraph Mining ». Dans Proceedings of the 2015 SIAM International Conference on Data Mining. Philadelphia, PA : Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974010.85.
Texte intégralKenig, Batya, Pranay Mundra, Guna Prasaad, Babak Salimi et Dan Suciu. « Mining Approximate Acyclic Schemes from Relations ». Dans SIGMOD/PODS '20 : International Conference on Management of Data. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3318464.3380573.
Texte intégralSilvestri, Claudio, et Salvatore Orlando. « Distributed approximate mining of frequent patterns ». Dans the 2005 ACM symposium. New York, New York, USA : ACM Press, 2005. http://dx.doi.org/10.1145/1066677.1066796.
Texte intégralSeo, San Ha, et Saeed Salem. « Mining representative approximate frequent coexpression subnetworks ». Dans BCB '20 : 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3388440.3415584.
Texte intégralAnchuri, Pranay, Mohammed J. Zaki, Omer Barkol, Shahar Golan et Moshe Shamy. « Approximate graph mining with label costs ». Dans KDD' 13 : The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA : ACM, 2013. http://dx.doi.org/10.1145/2487575.2487602.
Texte intégralWang, Lijun, Ming Dong et Alexander Kotov. « Multi-level Approximate Spectral Clustering ». Dans 2015 IEEE International Conference on Data Mining (ICDM). IEEE, 2015. http://dx.doi.org/10.1109/icdm.2015.38.
Texte intégralLi, Haifeng, Zongjian Lu et Hong Chen. « Mining Approximate Closed Frequent Itemsets over Stream ». Dans 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing. IEEE, 2008. http://dx.doi.org/10.1109/snpd.2008.32.
Texte intégralRapports d'organisations sur le sujet "Approximate Mining"
Weller, Joel I., Derek M. Bickhart, Micha Ron, Eyal Seroussi, George Liu et George R. Wiggans. Determination of actual polymorphisms responsible for economic trait variation in dairy cattle. United States Department of Agriculture, janvier 2015. http://dx.doi.org/10.32747/2015.7600017.bard.
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