Bibliographies thématiques / Multidimensional data mining

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Littérature scientifique sur le sujet « Multidimensional data mining »

Auteur : Grafiati
Publié le 10 mars 2023
Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Multidimensional data mining"

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Jiawei Han, L. V. S. Lakshmanan, and R. T. Ng. "Constraint-based, multidimensional data mining." Computer 32, no. 8 (1999): 46–50. http://dx.doi.org/10.1109/2.781634.

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Ekwe, Prince O., Mathew Okoronkwo, Tochi P. Ukwome, and Valentine U. Anozie. "Multidimensional Data Analysis, Data Mining and Knowledge Discovery." Multidimensional Data Analysis, Data Mining and Knowledge Discovery 9, no. 1 (2024): 6. https://doi.org/10.5281/zenodo.10656236.

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In recent times, the rate of usage and consumption of data has led to the need for these data to be organized, analyzed and used for futuristic prediction and decision making in order to improve human lives and future prediction in different fields of endeavor. Multidimensional Data Analysis, Data Mining and Knowledge Discovery are all associated with the organization, analysis and extraction of a data set for organization’s decision making and futuristic prediction. In this research work, our focus was on the techniques, application of data mining as well as the phases involved in data
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Bimonte, Sandro, Lucile Sautot, Ludovic Journaux, and Bruno Faivre. "Multidimensional Model Design using Data Mining." International Journal of Data Warehousing and Mining 13, no. 1 (2017): 1–35. http://dx.doi.org/10.4018/ijdwm.2017010101.

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Designing and building a Data Warehouse (DW), and associated OLAP cubes, are long processes, during which decision-maker requirements play an important role. But decision-makers are not OLAP experts and can find it difficult to deal with the concepts behind DW and OLAP. To support DW design in this context, we propose: (i) a new rapid prototyping methodology, integrating two different DM algorithms, to define dimension hierarchies according to decision-maker knowledge; (ii) a complete UML Profile, to define a DW schema that integrates both the DM algorithms; (iii) a mapping process to transfor
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Zhang, Chao, and Jiawei Han. "Multidimensional Mining of Massive Text Data." Synthesis Lectures on Data Mining and Knowledge Discovery 11, no. 2 (2019): 1–198. http://dx.doi.org/10.2200/s00903ed1v01y201902dmk017.

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Behnisch, Martin, and Alfred Ultsch. "Urban data-mining: spatiotemporal exploration of multidimensional data." Building Research & Information 37, no. 5-6 (2009): 520–32. http://dx.doi.org/10.1080/09613210903189343.

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Kim, Dae-In, Joon Park, Hong-Ki Kim, and Bu-Hyun Hwang. "Mining Association Rules in Multidimensional Stream Data." KIPS Transactions:PartD 13D, no. 6 (2006): 765–74. http://dx.doi.org/10.3745/kipstd.2006.13d.6.765.

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Chung-Ching Yu and Yen-Liang Chen. "Mining sequential patterns from multidimensional sequence data." IEEE Transactions on Knowledge and Data Engineering 17, no. 1 (2005): 136–40. http://dx.doi.org/10.1109/tkde.2005.13.

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Pawliczek, Piotr, and Witold Dzwinel. "Interactive Data Mining by Using Multidimensional Scaling." Procedia Computer Science 18 (2013): 40–49. http://dx.doi.org/10.1016/j.procs.2013.05.167.

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Gundem, Gunes, Christian Perez-Llamas, Alba Jene-Sanz, et al. "IntOGen: integration and data mining of multidimensional oncogenomic data." Nature Methods 7, no. 2 (2010): 92–93. http://dx.doi.org/10.1038/nmeth0210-92.

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Dzemyda, Gintautas, Virginijus Marcinkevičius, and Viktor Medvedev. "WEB APPLICATION FOR LARGE-SCALE MULTIDIMENSIONAL DATA VISUALIZATION." Mathematical Modelling and Analysis 16, no. 1 (2011): 273–85. http://dx.doi.org/10.3846/13926292.2011.580381.

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In this paper, we present an approach of the web application (as a service) for data mining oriented to the multidimensional data visualization. This paper focuses on visualization methods as a tool for the visual presentation of large-scale multidimensional data sets. The proposed implementation of such a web application obtains a multidimensional data set and as a result produces a visualization of this data set. It also supports different configuration parameters of the data mining methods used. Parallel computation has been used in the proposed implementation to run the algorithms simultan
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Thèses sur le sujet "Multidimensional data mining"

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Torre, Fabrizio. "3D data visualization techniques and applications for visual multidimensional data mining." Doctoral thesis, Universita degli studi di Salerno, 2014. http://hdl.handle.net/10556/1561.

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2012 - 2013<br>Despite modern technology provide new tools to measure the world around us, we are quickly generating massive amounts of high-dimensional, spatialtemporal data. In this work, I deal with two types of datasets: one in which the spatial characteristics are relatively dynamic and the data are sampled at different periods of time, and the other where many dimensions prevail, although the spatial characteristics are relatively static. The first dataset refers to a peculiar aspect of uncertainty arising from contractual relationships that regulate a project execution: the dispu
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Nimmagadda, Shastri Lakshman. "Ontology based data warehousing for mining of heterogeneous and multidimensional data sources." Thesis, Curtin University, 2015. http://hdl.handle.net/20.500.11937/2322.

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Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared i
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Wu, Hao-cun, and 吳浩存. "A multidimensional data model for monitoring web usage and optimizing website topology." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29528215.

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Peterson, Angela R. "Visual data mining Using parallel coordinate plots with K-means clustering and color to find correlations in a multidimensional dataset /." Instructions for remote access, 2009. http://www.kutztown.edu/library/services/remote_access.asp.

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Ding, Guoxiang. "DERIVING ACTIVITY PATTERNS FROM INDIVIDUAL TRAVEL DIARY DATA: A SPATIOTEMPORAL DATA MINING APPROACH." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1236777859.

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Li, Hsin-Fang. "DATA MINING AND PATTERN DISCOVERY USING EXPLORATORY AND VISUALIZATION METHODS FOR LARGE MULTIDIMENSIONAL DATASETS." UKnowledge, 2013. http://uknowledge.uky.edu/epb_etds/4.

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Oral health problems have been a major public health concern profoundly affecting people’s general health and quality of life. Given that oral health data is composed of several measurable dimensions including clinical measurements, socio-behavioral factors, genetic predispositions, self-reported assessments, and quality of life measures, strategies for analyzing multidimensional data are neither computationally straightforward nor efficient. Researchers face major challenges to identify tools that circumvent the processes of manually probing the data. The purpose of this dissertation is to pr
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Kucuktunc, Onur. "Result Diversification on Spatial, Multidimensional, Opinion, and Bibliographic Data." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1374148621.

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Foltýnová, Veronika. "Multidimenzionální analýza dat a zpracování analytického zobrazení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-376922.

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This thesis deals with the analysis and display of multidimensional data. In the theoretical part, the issue of data mining, its tasks and techniques, and a brief explanation of the terms Business Intelligence and data warehouse are presented. The issue of databases is also described in this thesis. Subsequently, the options for displaying multidimensional data are described. At the end of the theoretical part is briefly explained the problems of optical networks and especially the terms Gigabit passive optical network and its frame, because the data from the frames of this network will be dis
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Nunes, Santiago Augusto. "Análise espaço-temporal de data streams multidimensionais." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-17102016-152137/.

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Fluxos de dados são usualmente caracterizados por grandes quantidades de dados gerados continuamente em processos síncronos ou assíncronos potencialmente infinitos, em aplicações como: sistemas meteorológicos, processos industriais, tráfego de veículos, transações financeiras, redes de sensores, entre outras. Além disso, o comportamento dos dados tende a sofrer alterações significativas ao longo do tempo, definindo data streams evolutivos. Estas alterações podem significar eventos temporários (como anomalias ou eventos extremos) ou mudanças relevantes no processo de geração da stream (que resu
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Nieto, Erick Mauricio Gómez. "Projeção multidimensional aplicada a visualização de resultados de busca textual." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-05122012-105730/.

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Usuários da Internet estão muito familiarizados que resultados de uma consulta sejam exibidos como uma lista ordenada de snippets. Cada snippet possui conteúdo textual que mostra um resumo do documento referido (ou página web) e um link para o mesmo. Esta representação tem muitas vantagens como, por exemplo, proporcionar uma navegação fácil e simples de interpretar. No entanto, qualquer usuário que usa motores de busca poderia reportar possivelmente alguma experiência de decepção com este modelo. Todavia, ela tem limitações em situações particulares, como o não fornecimento de uma visão geral
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Livres sur le sujet "Multidimensional data mining"

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Zhang, Chao, and Jiawei Han. Multidimensional Mining of Massive Text Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01914-2.

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Adam, Schenker, ed. Graph-theoretic techniques for web content mining. World Scientific, 2005.

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Han, Jiawei, and Chao Zhang. Multidimensional Mining of Massive Text Data. Springer International Publishing AG, 2019.

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Han, Jiawei, and Chao Zhang. Multidimensional Mining of Massive Text Data. Morgan & Claypool Publishers, 2019.

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Han, Jiawei, and Chao Zhang. Multidimensional Mining of Massive Text Data. Morgan & Claypool Publishers, 2019.

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Han, Jiawei, and Chao Zhang. Multidimensional Mining of Massive Text Data. Morgan & Claypool Publishers, 2019.

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Grouping Multidimensional Data: Recent Advances in Clustering. Springer, 2006.

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Nicholas, Charles, Marc Teboulle, and Jacob Kogan. Grouping Multidimensional Data: Recent Advances in Clustering. Springer, 2010.

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Hurter, Christophe. Image-Based Visualization: Interactive Multidimensional Data Exploration. Morgan & Claypool Publishers, 2015.

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Hurter, Christophe. Image-Based Visualization: Interactive Multidimensional Data Exploration. Morgan & Claypool Publishers, 2016.

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Chapitres de livres sur le sujet "Multidimensional data mining"

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Tao, Fangbo. "Multidimensional Summarization." In Multidimensional Mining of Massive Text Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01914-2_6.

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Cyganek, Bogusław, and Michał Woźniak. "Efficient Multidimensional Pattern Recognition in Kernel Tensor Subspaces." In Data Mining and Big Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40973-3_54.

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Zdunek, Rafał, and Michalina Kotyla. "Extraction of Dynamic Nonnegative Features from Multidimensional Nonstationary Signals." In Data Mining and Big Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40973-3_57.

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Liu, Yi, Guoli Yang, Qibin Zheng, et al. "Ant-Antlion Optimizer with Similarity Information for Multidimensional Knapsack Problem." In Data Mining and Big Data. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0837-6_17.

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Günzel, Holger, Jens Albrecht, and Wolfgang Lehner. "Data Mining in a Multidimensional Environment." In Advances in Databases and Information Systems. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48252-0_15.

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Colonval, Jessy, and Fabrice Bouquet. "Multidimensional Adaptative kNN over Tracking Outliers (Makoto)." In Advanced Data Mining and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46661-8_36.

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Zhang, Chao, and Jiawei Han. "Introduction." In Multidimensional Mining of Massive Text Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01914-2_1.

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Zhang, Chao, and Jiawei Han. "Topic-Level Taxonomy Generation." In Multidimensional Mining of Massive Text Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01914-2_2.

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Zhang, Chao, and Jiawei Han. "Cross-Dimension Prediction in Cube Space." In Multidimensional Mining of Massive Text Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01914-2_7.

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Zhang, Yihao, Mehmet A. Orgun, Weiqiang Lin, and Rohan Baxter. "Mining Multidimensional Data through Element Oriented Analysis." In PRICAI 2008: Trends in Artificial Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89197-0_51.

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Actes de conférences sur le sujet "Multidimensional data mining"

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Yeh, Chin-Chia Michael, Audrey Der, Uday Singh Saini, et al. "Matrix Profile for Anomaly Detection on Multidimensional Time Series." In 2024 IEEE International Conference on Data Mining (ICDM). IEEE, 2024. https://doi.org/10.1109/icdm59182.2024.00114.

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Wang, Jimin, Shuaiwei Wang, and Junru Wang. "Research on CUDA-based Multidimensional Time Series Motif Mining." In 2024 5th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI). IEEE, 2024. https://doi.org/10.1109/iccbd-ai65562.2024.00036.

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Zhao, Chenguang, Qizhi Fang, and Yanpeng Sun. "Precision Teaching of EDA Technology Course in Multidimensional Data Space Based on Data Mining." In 2025 6th International Conference on Computer Science, Engineering, and Education (CSEE). IEEE, 2025. https://doi.org/10.1109/csee64583.2025.00026.

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Yan, Xu, Yaoting Jiang, Wenyi Liu, Didi Yi, and Jianjun Wei. "Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining." In 2024 5th International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI). IEEE, 2024. https://doi.org/10.1109/ichci63580.2024.10807860.

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Lu, Jingxian, Kunpeng Liu, Ying Wang, Yueze Li, and Zihan Zhao. "A time-series data integration method for power marketing operation and maintenance based on multidimensional data mining." In 2024 9th International Symposium on Computer and Information Processing Technology (ISCIPT). IEEE, 2024. http://dx.doi.org/10.1109/iscipt61983.2024.10673259.

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Gao, Xin, Shijie Wang, Zhanfeng Wang, Ying Liu, and Shiyu Song. "Mining of Behavioral Characteristics of Power Users Based on Multidimensional User Profiles and Big Data." In 2024 4th International Conference on Computer Science and Blockchain (CCSB). IEEE, 2024. http://dx.doi.org/10.1109/ccsb63463.2024.10735640.

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Dunstan, N., I. Despi, and C. Watson. "Anomalies in multidimensional contexts." In DATA MINING 2009. WIT Press, 2009. http://dx.doi.org/10.2495/data090181.

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Yokobayashi, Ryohei, and Takao Miura. "Multidimensional Data Mining Based on Tensor." In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. http://dx.doi.org/10.1109/icdmw.2018.00164.

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Pagani, Marco, Gloria Bordogna, and Massimiliano Valle. "Mining Multidimensional Data Using Clustering Techniques." In 18th International Conference on Database and Expert Systems Applications (DEXA 2007). IEEE, 2007. http://dx.doi.org/10.1109/dexa.2007.112.

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Pagani, Marco, Gloria Bordogna, and Massimiliano Valle. "Mining Multidimensional Data Using Clustering Techniques." In 18th International Conference on Database and Expert Systems Applications (DEXA 2007). IEEE, 2007. http://dx.doi.org/10.1109/dexa.2007.4312921.

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Rapports d'organisations sur le sujet "Multidimensional data mining"

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Traina, Agma, Caetano Traina, Spiros Papadimitriou, and Christos Faloutsos. Tri-Plots: Scalable Tools for Multidimensional Data Mining. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada459873.

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