Academic literature on the topic 'Hidden Data Mining'
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Journal articles on the topic "Hidden Data Mining"
Wang, Lidong, and Guanghui Wang. "Data Mining Applications in Big Data." Computer Engineering and Applications Journal 4, no. 3 (September 20, 2015): 143–52. http://dx.doi.org/10.18495/comengapp.v4i3.155.
Full textMaryoosh, Amal Abdulbaqi, and Enas Mohammed Hussein. "A Review: Data Mining Techniques and Its Applications." International Journal of Computer Science and Mobile Applications 10, no. 3 (March 30, 2022): 1–14. http://dx.doi.org/10.47760/ijcsma.2022.v10i03.001.
Full textSharma, Pragati, and Dr Sanjiv Sharma. "DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW." International Journal of Engineering Technologies and Management Research 5, no. 2 (May 1, 2020): 166–77. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.641.
Full textCarpenter, Chris. "Data Mining of Hidden Danger in Operational Production." Journal of Petroleum Technology 71, no. 08 (August 1, 2019): 71–78. http://dx.doi.org/10.2118/0819-0071-jpt.
Full textWang, Zhi Yan, Bei Zhan Wang, and Yi Dong Wang. "Data Mining Technology Applied in Network Security." Advanced Materials Research 989-994 (July 2014): 4974–79. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4974.
Full textLiu, Jing, Qing Xiang Zhu, Xin Yu, Jing Xin Wang, and Yi Ge Huang. "The Research of Warning Model of Hidden Failure Based on Data Mining." Key Engineering Materials 693 (May 2016): 1844–48. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1844.
Full textBathla, Gourav, Himanshu Aggarwal, and Rinkle Rani. "Migrating From Data Mining to Big Data Mining." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 13. http://dx.doi.org/10.14419/ijet.v7i3.4.14667.
Full textTang, Yu, and Guo Hui Li. "Data Mining and Visualization System Design and Development." Advanced Materials Research 971-973 (June 2014): 1444–48. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1444.
Full textChunfeng Liu, Shanshan Kong, Li Feng, and Yuqian Kang. "Outer P-sets and F- mining of Hidden Data." International Journal of Advancements in Computing Technology 4, no. 17 (September 30, 2012): 180–87. http://dx.doi.org/10.4156/ijact.vol4.issue17.21.
Full textGozali, Elahe, Bahlol Rahimi, Malihe Sadeghi, and Reza Safdari. "Diagnosis of diseases using data mining." Medical Technologies Journal 1, no. 4 (November 29, 2017): 120–21. http://dx.doi.org/10.26415/2572-004x-vol1iss4p120-121.
Full textDissertations / Theses on the topic "Hidden Data Mining"
Liu, Tantan. "Data Mining over Hidden Data Sources." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343313341.
Full textDharmavaram, Sirisha. "Mining Biomedical Data for Hidden Relationship Discovery." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538709/.
Full textLiu, Zhenjiao. "Incomplete multi-view data clustering with hidden data mining and fusion techniques." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS011.
Full textIncomplete multi-view data clustering is a research direction that attracts attention in the fields of data mining and machine learning. In practical applications, we often face situations where only part of the modal data can be obtained or there are missing values. Data fusion is an important method for incomplete multi-view information mining. Solving incomplete multi-view information mining in a targeted manner, achieving flexible collaboration between visible views and shared hidden views, and improving the robustness have become quite challenging. This thesis focuses on three aspects: hidden data mining, collaborative fusion, and enhancing the robustness of clustering. The main contributions are as follows:1. Hidden data mining for incomplete multi-view data: existing algorithms cannot make full use of the observation of information within and between views, resulting in the loss of a large amount of valuable information, and so we propose a new incomplete multi-view clustering model IMC-NLT (Incomplete Multi-view Clustering Based on NMF and Low-Rank Tensor Fusion) based on non-negative matrix factorization and low-rank tensor fusion. IMC-NLT first uses a low-rank tensor to retain view features with a unified dimension. Using a consistency measure, IMC-NLT captures a consistent representation across multiple views. Finally, IMC-NLT incorporates multiple learning into a unified model such that hidden information can be extracted effectively from incomplete views. We conducted comprehensive experiments on five real-world datasets to validate the performance of IMC-NLT. The overall experimental results demonstrate that the proposed IMC-NLT performs better than several baseline methods, yielding stable and promising results.2. Collaborative fusion for incomplete multi-view data: our approach to address this issue is Incomplete Multi-view Co-Clustering by Sparse Low-Rank Representation (CCIM-SLR). The algorithm is based on sparse low-rank representation and subspace representation, in which jointly missing data is filled using data within a modality and related data from other modalities. To improve the stability of clustering results for multi-view data with different missing degrees, CCIM-SLR uses the Γ-norm model, which is an adjustable low-rank representation method. CCIM-SLR can alternate between learning the shared hidden view, visible view, and cluster partitions within a co-learning framework. An iterative algorithm with guaranteed convergence is used to optimize the proposed objective function. Compared with other baseline models, CCIM-SLR achieved the best performance in the comprehensive experiments on the five benchmark datasets, particularly on those with varying degrees of incompleteness.3. Enhancing the clustering robustness for incomplete multi-view data: we offer a fusion of graph convolution and information bottlenecks (Incomplete Multi-view Representation Learning Through Anchor Graph-based GCN and Information Bottleneck - IMRL-AGI). First, we introduce the information bottleneck theory to filter out the noise data with irrelevant details and retain only the most relevant feature items. Next, we integrate the graph structure information based on anchor points into the local graph information of the state fused into the shared information representation and the information representation learning process of the local specific view, a process that can balance the robustness of the learned features and improve the robustness. Finally, the model integrates multiple representations with the help of information bottlenecks, reducing the impact of redundant information in the data. Extensive experiments are conducted on several real-world datasets, and the results demonstrate the superiority of IMRL-AGI. Specifically, IMRL-AGI shows significant improvements in clustering and classification accuracy, even in the presence of high view missing rates (e.g. 10.23% and 24.1% respectively on the ORL dataset)
Peng, Yingli. "Improvement of Data Mining Methods on Falling Detection and Daily Activities Recognition." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-25521.
Full textYang, Yimin. "Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2254.
Full textSajeva, Lisa. "Predizione del tempo rimanente di vita di un impianto mediante Hidden Markow Model." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13846/.
Full textVitali, Federico. "Map-Matching su Piattaforma BigData." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18089/.
Full textEng, Catherine. "Développement de méthodes de fouille de données basées sur les modèles de Markov cachés du second ordre pour l'identification d'hétérogénéités dans les génomes bactériens." Thesis, Nancy 1, 2010. http://www.theses.fr/2010NAN10041/document.
Full textSecond-order Hidden Markov Models (HMM2) are stochastic processes with a high efficiency in exploring bacterial genome sequences. Different types of HMM2 (M1M2, M2M2, M2M0) combined to combinatorial methods were developed in a new approach to discriminate genomic regions without a priori knowledge on their genetic content. This approach was applied on two bacterial models in order to validate its achievements: Streptomyces coelicolor and Streptococcus thermophilus. These bacterial species exhibit distinct genomic traits (base composition, global genome size) in relation with their ecological niche: soil for S. coelicolor and dairy products for S. thermophilus. In S. coelicolor, a first HMM2 architecture allowed the detection of short discrete DNA heterogeneities (5-16 nucleotides in size), mostly localized in intergenic regions. The application of the method on a biologically known gene set, the SigR regulon (involved in oxidative stress response), proved the efficiency in identifying bacterial promoters. S. coelicolor shows a complex regulatory network (up to 12% of the genes may be involved in gene regulation) with more than 60 sigma factors, involved in initiation of transcription. A classification method coupled to a searching algorithm (i.e. R’MES) was developed to automatically extract the box1-spacer-box2 composite DNA motifs, structure corresponding to the typical bacterial promoter -35/-10 boxes. Among the 814 DNA motifs described for the whole S. coelicolor genome, those of sigma factors (B, WhiG) could be retrieved from the crude data. We could show that this method could be generalized by applying it successfully in a preliminary attempt to the genome of Bacillus subtilis
陳迪祥. "A Data Mining Approach to Eliciting Hidden Relationships from Disease Data." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/33856707588342488454.
Full text國立暨南國際大學
資訊管理學系
91
Data mining is able to find some unobvious or hidden information from data and it is what the managers of hospitals need for their rich data. There are many kinds of data in those hospitals’ database, such as records of emergency treatment, records of outpatient services, records of examining patients, and records of taking medicines. The data is helpful for exploring medical knowledge by data mining technology. This paper describes a data mining system which processing the standard health insurance files defined by Bureau of National Health Insurance. The system uses FP-Tree for good performance of mining. A distributed and caching architecture has been implemented in the system to balance the loading of mining. Users can acquire mining results from the system quickly. The system will elicit hidden relationships within diseases from those health insurance files. Our frequent patterns also include conditional probabilities that certain diseases may happen if the patient has some disease. Doctors and researchers operate the system by a browser. The mining results discovered by the system will help doctors and researchers with medical researches. Keywords: Data mining, Health Insurance, Medicine, Distributed Architecture
Yu, Zhun. "Mining Hidden Knowledge from Measured Data for Improving Building Energy Performance." Thesis, 2012. http://spectrum.library.concordia.ca/973713/1/Yu_PhD_S2012.pdf.
Full textBooks on the topic "Hidden Data Mining"
Big data analytics with R: Utilize R to uncover hidden patterns in your big data. Birmingham, UK: Packt Publishing, 2016.
Find full textUnited States. Congress. Senate. Committee on Homeland Security and Governmental Affairs. Permanent Subcommittee on Investigations. Online advertising and hidden hazards to consumer security and data privacy: Hearing before the Permanent Subcommittee on Investigations of the Committee on Homeland Security and Governmental Affairs, United States Senate, One Hundred Thirteenth Congress, second session, May 15, 2014. Washington: U.S. Government Printing Office, 2014.
Find full textDubner, Stephen J. Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. New York, USA: Harper Torch, 2006.
Find full textLevitt, Steven D., and Stephen J. Dubner. Freakonomics: A rogue economist explores the hidden side of everything. New York: William Morrow, 2007.
Find full textLevitt, Steven D., and Stephen J. Dubner. Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. New York, USA: William Morrow, 2006.
Find full textLevitt, Steven D. Freakonomics: A rogue economist explores the hidden side of everything. New York: William Morrow, 2005.
Find full textLevitt, Steven D., and Stephen J. Dubner. Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. 7th ed. New York: William Morrow, 2007.
Find full textLevitt, Steven D. Freakonomics: A rogue economist explores the hidden side of everything. New York: Harper Perennial, 2009.
Find full textLevitt, Steven D., and Stephen J. Dubner. Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. New York: William Morrow, 2005.
Find full textLevitt, Steven D. Freakonomics: A rogue economist explores the hidden side of everything. New York: Harper Perennial, 2009.
Find full textBook chapters on the topic "Hidden Data Mining"
Bosch, Antal van den. "Hidden Markov Models." In Encyclopedia of Machine Learning and Data Mining, 1–3. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_124-1.
Full textvan den Bosch, Antal. "Hidden Markov Models." In Encyclopedia of Machine Learning and Data Mining, 609–11. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_124.
Full textFeng, Shi, Daling Wang, Ge Yu, Chao Yang, and Nan Yang. "Chinese Blog Clustering by Hidden Sentiment Factors." In Advanced Data Mining and Applications, 140–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03348-3_16.
Full textŽliobaitė, Indrė. "Identifying Hidden Contexts in Classification." In Advances in Knowledge Discovery and Data Mining, 277–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20841-6_23.
Full textZhou, Weida, Li Zhang, and Licheng Jiao. "Hidden Space Principal Component Analysis." In Advances in Knowledge Discovery and Data Mining, 801–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731139_93.
Full textNie, Jinhui, Hongqi Su, and Xiaohua Zhou. "Research on Map Matching Based on Hidden Markov Model." In Advanced Data Mining and Applications, 277–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-53914-5_24.
Full textLin, Weiqiang, and Mehmet A. Orgun. "Temporal Data Mining Using Hidden Periodicity Analysis." In Lecture Notes in Computer Science, 49–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-39963-1_6.
Full textAdibi, Jafar, and Wei-Min Shen. "Self-Similar Layered Hidden Markov Models." In Principles of Data Mining and Knowledge Discovery, 1–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44794-6_1.
Full textYu, Jeffrey Xu. "Finding Hidden Structures in Relational Databases." In Advances in Knowledge Discovery and Data Mining, 2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_2.
Full textLi, Xingjuan, Yu Li, and Jiangtao Cui. "Estimating Interactions of Functional Brain Connectivity by Hidden Markov Models." In Advanced Data Mining and Applications, 403–12. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05090-0_34.
Full textConference papers on the topic "Hidden Data Mining"
Bhuiyan, Mansurul, Snehasis Mukhopadhyay, and Mohammad Al Hasan. "Interactive pattern mining on hidden data." In the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2396777.
Full textDharmavaram, Sirisha, Arshad Shaik, and Wei Jin. "Mining Biomedical Data for Hidden Relationship Discovery." In 2019 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2019. http://dx.doi.org/10.1109/ichi.2019.8904747.
Full textDautriche, Remy, Alexandre Termier, Renaud Blanch, and Miguel Santana. "Towards Visualizing Hidden Structures." In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0171.
Full textBerti-Equille, Laure, Ji Meng Loh, and Tamraparni Dasu. "A Masking Index for Quantifying Hidden Glitches." In 2013 IEEE International Conference on Data Mining (ICDM). IEEE, 2013. http://dx.doi.org/10.1109/icdm.2013.16.
Full textNazi, Azade, Saravanan Thirumuruganathan, Vagelis Hristidis, Nan Zhang, Khaled Shaban, and Gautam Das. "Query Hidden Attributes in Social Networks." In 2014 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, 2014. http://dx.doi.org/10.1109/icdmw.2014.113.
Full textJiang, Zhe, and Arpan Man Sainju. "Hidden Markov Contour Tree." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3330878.
Full textSato, Makoto, and Shuuichiro Imahara. "Clustering Geospatial Objects via Hidden Markov Random Fields." In 2008 Eighth IEEE International Conference on Data Mining (ICDM). IEEE, 2008. http://dx.doi.org/10.1109/icdm.2008.70.
Full textYoshida, Tetsuya. "Toward Finding Hidden Communities Based on User Profile." In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.20.
Full textSiraj, Fadzilah, and Mansour Ali Abdoulha. "Uncovering Hidden Information Within University's Student Enrollment Data Using Data Mining." In 2009 Third Asia International Conference on Modelling & Simulation. IEEE, 2009. http://dx.doi.org/10.1109/ams.2009.117.
Full textBelth, Caleb, Alican Buyukcakir, and Danai Koutra. "A Hidden Challenge of Link Prediction: Which Pairs to Check?" In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00092.
Full textReports on the topic "Hidden Data Mining"
Bond, W., Maria Seale, and Jeffrey Hensley. A dynamic hyperbolic surface model for responsive data mining. Engineer Research and Development Center (U.S.), April 2022. http://dx.doi.org/10.21079/11681/43886.
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