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Статті в журналах з теми "Mining Frequent Patterns"
Han, Jiawei, and Jian Pei. "Mining frequent patterns by pattern-growth." ACM SIGKDD Explorations Newsletter 2, no. 2 (December 2000): 14–20. http://dx.doi.org/10.1145/380995.381002.
Повний текст джерелаS., Sivaranjani. "Detecting Congestion Patterns in Spatio Temporal Traffic Data Using Frequent Pattern Mining." Bonfring International Journal of Networking Technologies and Applications 5, no. 1 (March 30, 2018): 21–23. http://dx.doi.org/10.9756/bijnta.8372.
Повний текст джерелаKRIBII, Rajae, and Youssef FAKIR. "Mining Frequent Sequential Patterns." Journal of Big Data Research 1, no. 2 (March 15, 2021): 20–37. http://dx.doi.org/10.14302/issn.2768-0207.jbr-21-3455.
Повний текст джерелаHuang, Hao, Xindong Wu, and Richard Relue. "Mining frequent patterns with the pattern tree." New Generation Computing 23, no. 4 (December 2005): 315–37. http://dx.doi.org/10.1007/bf03037636.
Повний текст джерелаAbdelaal, Areej Ahmad, Sa'ed Abed, Mohammad Al-Shayeji, and Mohammad Allaho. "Customized frequent patterns mining algorithms for enhanced Top-Rank-K frequent pattern mining." Expert Systems with Applications 169 (May 2021): 114530. http://dx.doi.org/10.1016/j.eswa.2020.114530.
Повний текст джерелаOtt, Jurg, and Taesung Park. "Overview of frequent pattern mining." Genomics & Informatics 20, no. 4 (December 31, 2022): e39. http://dx.doi.org/10.5808/gi.22074.
Повний текст джерелаYun, Unil, and Eunchul Yoon. "An Efficient Approach for Mining Weighted Approximate Closed Frequent Patterns Considering Noise Constraints." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no. 06 (December 2014): 879–912. http://dx.doi.org/10.1142/s0218488514500470.
Повний текст джерелаSantoro, 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.
Повний текст джерелаAida 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 (September 12, 2018): 134. http://dx.doi.org/10.14419/ijet.v7i4.1.28241.
Повний текст джерелаXue, 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 (August 5, 2019): 574–89. http://dx.doi.org/10.15837/ijccc.2019.4.3607.
Повний текст джерелаДисертації з теми "Mining Frequent Patterns"
Soztutar, Enis. "Mining Frequent Semantic Event Patterns." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611007/index.pdf.
Повний текст джерелаplay video event'
with properties '
video'
, '
length of video'
, '
name of video'
, etc. When the event objects belong to the domain model of the web site'
s ontology, they are referred as semantic events. In this work, we propose a new algorithm and associated framework for mining patterns of semantic events from the usage logs. We present a method for tracking and logging domain-level events of a web site, adding semantic information to events, an ordering of events in respect to the genericity of the event, and an algorithm for computing sequences of frequent events.
Jin, Ruoming. "New techniques for efficiently discovering frequent patterns." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1121795612.
Повний текст джерелаTitle from first page of PDF file. Document formatted into pages; contains xvii, 170 p.; also includes graphics. Includes bibliographical references (p. 160-170). Available online via OhioLINK's ETD Center
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.
Повний текст джерелаBifet, Albert. "Adaptive Learning and Mining for Data Streams and Frequent Patterns." Doctoral thesis, Universitat Politècnica de Catalunya, 2009. http://hdl.handle.net/10803/22738.
Повний текст джерелаThis thesis is devoted to the design of data mining algorithms for evolving data streams and for the extraction of closed frequent trees. First, we deal with each of these tasks separately, and then we deal with them together, developing classification methods for data streams containing items that are trees. In the data stream model, data arrive at high speed, and the algorithms that must process them have very strict constraints of space and time. In the first part of this thesis we propose and illustrate a framework for developing algorithms that can adaptively learn from data streams that change over time. Our methods are based on using change detectors and estimator modules at the right places. We propose an adaptive sliding window algorithm ADWIN for detecting change and keeping updated statistics from a data stream, and use it as a black-box in place or counters or accumulators in algorithms initially not designed for drifting data. Since ADWIN has rigorous performance guarantees, this opens the possibility of extending such guarantees to learning and mining algorithms. We test our methodology with several learning methods as Naïve Bayes, clustering, decision trees and ensemble methods. We build an experimental framework for data stream mining with concept drift, based on the MOA framework, similar to WEKA, so that it will be easy for researchers to run experimental data stream benchmarks. Trees are connected acyclic graphs and they are studied as link-based structures in many cases. In the second part of this thesis, we describe a rather formal study of trees from the point of view of closure-based mining. Moreover, we present efficient algorithms for subtree testing and for mining ordered and unordered frequent closed trees. We include an analysis of the extraction of association rules of full confidence out of the closed sets of trees, and we have found there an interesting phenomenon: rules whose propositional counterpart is nontrivial are, however, always implicitly true in trees due to the peculiar combinatorics of the structures. And finally, using these results on evolving data streams mining and closed frequent tree mining, we present high performance algorithms for mining closed unlabeled rooted trees adaptively from data streams that change over time. We introduce a general methodology to identify closed patterns in a data stream, using Galois Lattice Theory. Using this methodology, we then develop an incremental one, a sliding-window based one, and finally one that mines closed trees adaptively from data streams. We use these methods to develop classification methods for tree data streams.
Bifet, Figuerol Albert Carles. "Adaptive Learning and Mining for Data Streams and Frequent Patterns." Doctoral thesis, Universitat Politècnica de Catalunya, 2009. http://hdl.handle.net/10803/22738.
Повний текст джерелаThis thesis is devoted to the design of data mining algorithms for evolving data streams and for the extraction of closed frequent trees. First, we deal with each of these tasks separately, and then we deal with them together, developing classification methods for data streams containing items that are trees. In the data stream model, data arrive at high speed, and the algorithms that must process them have very strict constraints of space and time. In the first part of this thesis we propose and illustrate a framework for developing algorithms that can adaptively learn from data streams that change over time. Our methods are based on using change detectors and estimator modules at the right places. We propose an adaptive sliding window algorithm ADWIN for detecting change and keeping updated statistics from a data stream, and use it as a black-box in place or counters or accumulators in algorithms initially not designed for drifting data. Since ADWIN has rigorous performance guarantees, this opens the possibility of extending such guarantees to learning and mining algorithms. We test our methodology with several learning methods as Naïve Bayes, clustering, decision trees and ensemble methods. We build an experimental framework for data stream mining with concept drift, based on the MOA framework, similar to WEKA, so that it will be easy for researchers to run experimental data stream benchmarks. Trees are connected acyclic graphs and they are studied as link-based structures in many cases. In the second part of this thesis, we describe a rather formal study of trees from the point of view of closure-based mining. Moreover, we present efficient algorithms for subtree testing and for mining ordered and unordered frequent closed trees. We include an analysis of the extraction of association rules of full confidence out of the closed sets of trees, and we have found there an interesting phenomenon: rules whose propositional counterpart is nontrivial are, however, always implicitly true in trees due to the peculiar combinatorics of the structures. And finally, using these results on evolving data streams mining and closed frequent tree mining, we present high performance algorithms for mining closed unlabeled rooted trees adaptively from data streams that change over time. We introduce a general methodology to identify closed patterns in a data stream, using Galois Lattice Theory. Using this methodology, we then develop an incremental one, a sliding-window based one, and finally one that mines closed trees adaptively from data streams. We use these methods to develop classification methods for tree data streams.
Seyfi, Majid. "Mining discriminative itemsets in data streams using different window models." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/120850/1/Majid_Seyfi_Thesis.pdf.
Повний текст джерелаEl-Sayed, Maged F. "An efficient and incremental system to mine contiguous frequent sequences." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0130104-115506.
Повний текст джерелаMeng, Jinghan. "Flexible and Feasible Support Measures for Mining Frequent Patterns in Large Labeled Graphs." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6900.
Повний текст джерелаKilic, Sefa. "Clustering Frequent Navigation Patterns From Website Logs Using Ontology And Temporal Information." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12613979/index.pdf.
Повний текст джерелаTATAVARTY, GIRIDHAR. "FINDING TEMPORAL ASSOCIATION RULES BETWEEN FREQUENT PATTERNS IN MULTIVARIATE TIME SERIES." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141325950.
Повний текст джерелаКниги з теми "Mining Frequent Patterns"
Aggarwal, Charu C., and Jiawei Han, eds. Frequent Pattern Mining. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2.
Повний текст джерелаAydin, Berkay, and Rafal A. Angryk. Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99873-2.
Повний текст джерелаIndian Institute of Management, Ahmedabad., ed. An efficient algorithm for frequent pattern mining for real-time business intelligence analytics in dense datasets. Ahmedabad: Indian Institute of Management, 2005.
Знайти повний текст джерелаAggarwal, Charu C., and Jiawei Han. Frequent Pattern Mining. Springer London, Limited, 2014.
Знайти повний текст джерелаAggarwal, Charu C., and Jiawei Han. Frequent Pattern Mining. Springer, 2016.
Знайти повний текст джерелаFrequent Pattern Mining. Springer, 2014.
Знайти повний текст джерела“Data Mining Concepts & Techniques”. 3rd ed. Morgan Kaufmann Publishers, 2011.
Знайти повний текст джерелаAydin, Berkay, and Rafal A. Angryk. Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories. Springer, 2018.
Знайти повний текст джерелаЧастини книг з теми "Mining Frequent Patterns"
Vreeken, Jilles, and Nikolaj Tatti. "Interesting Patterns." In Frequent Pattern Mining, 105–34. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_5.
Повний текст джерелаWu, Di. "Frequent Patterns." In Data Mining with Python, 356–69. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003462781-9.
Повний текст джерелаCheng, Hong, Xifeng Yan, and Jiawei Han. "Mining Graph Patterns." In Frequent Pattern Mining, 307–38. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_13.
Повний текст джерелаZhu, Feida. "Mining Long Patterns." In Frequent Pattern Mining, 83–104. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_4.
Повний текст джерелаvan Leeuwen, Matthijs, and Jilles Vreeken. "Mining and Using Sets of Patterns through Compression." In Frequent Pattern Mining, 165–98. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_8.
Повний текст джерелаDeng, Zhi-Hong, Cong-Rui Ji, Ming Zhang, and Shi-Wei Tang. "Mining Frequent Ordered Patterns." In Advances in Knowledge Discovery and Data Mining, 150–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11430919_19.
Повний текст джерелаTseng, Fan-Chen, and Ching-Chi Hsu. "Generating Frequent Patterns with the Frequent Pattern List." In Advances in Knowledge Discovery and Data Mining, 376–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45357-1_40.
Повний текст джерелаNofong, Vincent Mwintieru, Hamidu Abdel-Fatao, Michael Kofi Afriyie, and John Wondoh. "Discovering Self-reliant Periodic Frequent Patterns." In Periodic Pattern Mining, 105–31. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3964-7_7.
Повний текст джерелаLonlac, Jerry, Arnaud Doniec, Marin Lujak, and Stephane Lecoeuche. "Mining Frequent Seasonal Gradual Patterns." In Big Data Analytics and Knowledge Discovery, 197–207. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59065-9_16.
Повний текст джерелаAhmed, Usman, Jerry Chun-Wei Lin, and Philippe Fournier-Viger. "Privacy Preservation of Periodic Frequent Patterns Using Sensitive Inverse Frequency." In Periodic Pattern Mining, 215–27. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3964-7_12.
Повний текст джерелаТези доповідей конференцій з теми "Mining Frequent Patterns"
Qunxiong Zhu and Xiaoyong Lin. "Mining Frequent Patterns with Incremental Updating Frequent Pattern Tree." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1714215.
Повний текст джерелаZhou, Zhongmei. "Mining Frequent Independent Patterns and Frequent Correlated Patterns Synchronously." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.27.
Повний текст джерелаJunyan Zhang and Fan Min. "Mining frequent patterns from sequences." In 2011 2nd International Conference on Control, Instrumentation, and Automation (ICCIA). IEEE, 2011. http://dx.doi.org/10.1109/icciautom.2011.6183913.
Повний текст джерелаZhang, Junyan, and Fan Min. "Mining Frequent Patterns From Sequences." In 2013 2nd International Conference on Intelligent System and Applied Material. Ottawa: EDUGAIT Press, 2013. http://dx.doi.org/10.12696/gsam.2013.0830.
Повний текст джерелаMeng, Hui, Lifa Wu, Tianlei Zhang, Guisheng Chen, and Deyi Li. "Mining Frequent Composite Service Patterns." In 2008 Seventh International Conference on Grid and Cooperative Computing (GCC). IEEE, 2008. http://dx.doi.org/10.1109/gcc.2008.102.
Повний текст джерелаZhu, 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.
Повний текст джерелаBashir, Shariq, Zahid Halim, and A. Rauf Baig. "Mining fault tolerant frequent patterns using pattern growth approach." In 2008 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA). IEEE, 2008. http://dx.doi.org/10.1109/aiccsa.2008.4493532.
Повний текст джерелаYildiz, Baris, and Hatice Selale. "Mining frequent patterns from microarray data." In 2011 6th International Symposium on Health Informatics and Bioinformatics (HIBIT). IEEE, 2011. http://dx.doi.org/10.1109/hibit.2011.6450819.
Повний текст джерелаYen, Show-Jane, Yue-Shi Lee, Yu-Ting Guo, and Jia-Yuan Gu. "Mining frequent patterns from incremental databases." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6016712.
Повний текст джерелаDeng, Zhi-Hong, and Guo-Dong Fang. "Mining Top-Rank-K Frequent Patterns." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370261.
Повний текст джерелаЗвіти організацій з теми "Mining Frequent Patterns"
Shekhar, Shashi, Pradeep Mohan, Dev Oliver, and Xun Zhou. Crime Pattern Analysis: A Spatial Frequent Pattern Mining Approach. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada561517.
Повний текст джерела