Articles de revues : « 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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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 transform multidimensional schemata according to the results of the DM algorithms; (iv) a tool implementing the proposed methodology; (v) a full validation, based on a real case study concerning bird biodiversity. In conclusion, we confirm the rapidity and efficacy of our methodology and tool in providing a multidimensional schema to satisfy decision-maker analytical needs.

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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 simultaneously on different computers.

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kumar, Santhosh, and E. Ramaraj. "A Hybrid Model for Mining Multidimensional Data Sets." International Journal of Computer Applications Technology and Research 2, no. 3 (2013): 214–17. http://dx.doi.org/10.7753/ijcatr0203.1001.

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Tang, Guanting, Jian Pei, James Bailey, and Guozhu Dong. "Mining multidimensional contextual outliers from categorical relational data." Intelligent Data Analysis 19, no. 5 (2015): 1171–92. http://dx.doi.org/10.3233/ida-150764.

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Zheng, Zheng, Jun Pei, Nupur Bansal, Hao Liu, Lin Frank Song, and Kenneth M. Merz. "Generation of Pairwise Potentials Using Multidimensional Data Mining." Journal of Chemical Theory and Computation 14, no. 10 (2018): 5045–67. http://dx.doi.org/10.1021/acs.jctc.8b00516.

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Choong, Yeow Wei, Anne Laurent, and Dominique Laurent. "Mining multiple-level fuzzy blocks from multidimensional data." Fuzzy Sets and Systems 159, no. 12 (2008): 1535–53. http://dx.doi.org/10.1016/j.fss.2008.01.011.

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Plantevit, M., A. Laurent, and M. Teisseire. "Mining convergent and divergent sequences in multidimensional data." International Journal of Business Intelligence and Data Mining 4, no. 3/4 (2009): 242. http://dx.doi.org/10.1504/ijbidm.2009.029074.

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Hong Zhou. "The Study on Data Mining based on Multidimensional-Data Flow." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 7 (2013): 851–57. http://dx.doi.org/10.4156/aiss.vol5.issue7.100.

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Li-li, Chen, Fu Xiao-juan, Gang Jia-lin, and Lin Li. "A New Data Mining Method based on Multidimensional-Data Flow." Procedia Engineering 24 (2011): 365–69. http://dx.doi.org/10.1016/j.proeng.2011.11.2658.

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Xie, Jiangning, Feng Xu, Zhen Li, and Xueqing Li. "Data Mining Method under Model-Driven Architecture (MDA)." Security and Communication Networks 2022 (March 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/5806829.

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With the development of university information technology, how to mine and visually analyze the data of the existing separated information system will become an important research topic. The current university information system is a combination of some proprietary business systems characterized by poor data separation and storage and data analysis power. In addition, the data mining methods based on cloud computing will make customers gradually lose the ability to control the data. Because of the above problems, this paper proposes a university data mining method based on the MDA idea by constructing a data analysis and visualization framework, including multidimensional data modeling, data extraction, and data display based on visualization technology. The framework makes full use of the design idea of MDA and models multidimensional data, data extraction, and data display, respectively. The multidimensional data model module, data extraction module, and data visualization module provide efficient solutions for data analysis and visualization in universities.

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Oellien, Frank, Wolf-Dietrich Ihlenfeldt, and Johann Gasteiger. "InfVis − Platform-Independent Visual Data Mining of Multidimensional Chemical Data Sets." Journal of Chemical Information and Modeling 45, no. 5 (2005): 1456–67. http://dx.doi.org/10.1021/ci050202k.

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Sukaesih Sitanggang, Imas. "Spatial Multidimensional Association Rules Mining in Forest Fire Data." Journal of Data Analysis and Information Processing 01, no. 04 (2013): 90–96. http://dx.doi.org/10.4236/jdaip.2013.14010.

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Solntseva-Chaley, Maria. "New data mining technique for multidimensional aircraft trajectories analysis." ITM Web of Conferences 8 (2016): 01001. http://dx.doi.org/10.1051/itmconf/20160801001.

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Chiang, Johannes K., and Chia-Chi Chu. "Multidimensional Multi-granularities Data Mining for Discover Association Rule." Transactions on Machine Learning and Artificial Intelligence 2, no. 3 (2014): 73–89. http://dx.doi.org/10.14738/tmlai.23.259.

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Chen, Yu Ke, and Tai Xiang Zhao. "Association Rule Mining Based on Multidimensional Pattern Relations." Advanced Materials Research 918 (April 2014): 243–45. http://dx.doi.org/10.4028/www.scientific.net/amr.918.243.

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Most incremental mining and online mining algorithms concentrate on finding association rules or patterns consistent with entire current sets of data. Users cannot easily obtain results from only interesting portion of data. This may prevent the usage of mining from online decision support for multidimensional data. To provide adhoc, query driven, and online mining support, we first propose a relation called the multidimensional pattern relation to structurally and systematically store context and mining information for later analysis.

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Casali, Alain, Sébastien Nedjar, Rosine Cicchetti, and Lotfi Lakhal. "Constrained Cube Lattices for Multidimensional Database Mining." International Journal of Data Warehousing and Mining 6, no. 3 (2010): 43–72. http://dx.doi.org/10.4018/jdwm.2010070104.

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In multidimensional database mining, constrained multidimensional patterns differ from the well-known frequent patterns from both conceptual and logical points of view because of a common structure and the ability to support various types of constraints. Classical data mining techniques are based on the power set lattice of binary attribute values and, even adapted, are not suitable when addressing the discovery of constrained multidimensional patterns. In this paper, the authors propose a foundation for various multidimensional database mining problems by introducing a new algebraic structure called cube lattice, which characterizes the search space to be explored. This paper takes into consideration monotone and/or anti-monotone constraints enforced when mining multidimensional patterns. The authors propose condensed representations of the constrained cube lattice, which is a convex space, and present a generalized levelwise algorithm for computing them. Additionally, the authors consider the formalization of existing data cubes, and the discovery of frequent multidimensional patterns, while introducing a perfect concise representation from which any solution provided with its conjunction, disjunction and negation frequencies. Finally, emphasis on advantages of the cube lattice when compared to the power set lattice of binary attributes in multidimensional database mining are placed.

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Zhu, Jian Xin. "An Improved Concept Lattice-Based Data Mining Algorithm." Applied Mechanics and Materials 687-691 (November 2014): 1214–17. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1214.

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In order to solve the multidimensional data model and relational data model,query between the two-way data system, data cleansing, data conversion, distributed data accuracy and consistency control problem, this paper described the concept of grid related, the global data mining combined with local data mining is proposed based on local information based on the concept of a global grid of data mining algorithm, and the mining process was divided into ETI. Action, combined with the ETI. Process workflow, using amounts of data distributed parallel sequence mining. Experiments show that the algorithm has a good effect on enhanced data processing capability.

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Liu, Yafei, Jiye Li, Zhaoxu Ren, and Jun Li. "Research on Personalized Recommendation of Higher Education Resources Based on Multidimensional Association Rules." Wireless Communications and Mobile Computing 2022 (April 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/2922091.

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The personalized recommendation method of higher education resources currently cannot carry out multidimensional association analysis of learners, situations, and resources and cannot extract accurate resources for learners, resulting in a large error. This study constructs a personalized recommendation method for higher education resources based on multidimensional association rules. This algorithm clarifies the multidimensional association rules, extracts the key data from massive educational resources, and groups the same kind of data by using a frequent itemset algorithm for mining association rules, namely, the Apriori algorithm. Combined with traditional data mining technology, this study constructs a new personalized recommendation model for education resources based on multidimensional association rules, which achieves the accurate extraction of higher education resources and ensures the matching degree between learners and resources. The experimental results show that the personalized recommendation model of educational resources in this study effectively makes up for the disadvantages of the traditional data mining algorithms, with a small root mean square error and short data mining time, within 20 ms.

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Gayathri, B., and Dr E. Ramaraj. "Mining Multidimensional Data Using Constraint Frequent Pattern in Medical Dataset." International Journal of Computer Trends and Technology 13, no. 2 (2014): 92–94. http://dx.doi.org/10.14445/22312803/ijctt-v13p120.

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Pradhan, Gaurav N., and B. Prabhakaran. "Association Rule Mining in Multiple, Multidimensional Time Series Medical Data." Journal of Healthcare Informatics Research 1, no. 1 (2017): 92–118. http://dx.doi.org/10.1007/s41666-017-0001-x.

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Gyenesei, Attila, and Jukka Teuhola. "Multidimensional fuzzy partitioning of attribute ranges for mining quantitative data." International Journal of Intelligent Systems 19, no. 11 (2004): 1111–26. http://dx.doi.org/10.1002/int.20039.

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Goil, Sanjay, and Alok Choudhary. "PARSIMONY: An Infrastructure for Parallel Multidimensional Analysis and Data Mining." Journal of Parallel and Distributed Computing 61, no. 3 (2001): 285–321. http://dx.doi.org/10.1006/jpdc.2000.1691.

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Li, Zhenxin. "Digital library archives information integration based on multidimensional data mining." International Journal of Reasoning-based Intelligent Systems 14, no. 4 (2022): 169. http://dx.doi.org/10.1504/ijris.2022.126659.

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Liu, Mou Zhong, and Min Sun. "Application of Multidimensional Data Model in the Traffic Accident Data Warehouse." Applied Mechanics and Materials 548-549 (April 2014): 1857–61. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1857.

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The traffic administrative department would record real-time information of accidents and update the corresponding database when dealing with daily traffic routines. It is of great significance to study and analyze these data. In this paper, we propose a Multi-dimensional Data Warehouse Model (M-DWM) combined with the concept of Data Warehouse and multi-dimensional data processing theory. The model can greatly improve the efficiency for statistical analysis and data mining.

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Li, Haifeng. "Multidimensional Information Network Big Data Mining Algorithm Relying on Finite Element Analysis." Computational Intelligence and Neuroscience 2022 (April 11, 2022): 1–11. http://dx.doi.org/10.1155/2022/7156715.

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In recent years, with the rapid development of the Internet, online social networks have been continuously integrated with traditional interpersonal networks and research on information dissemination in social networks has gradually increased. This article studies and analyzes the multidimensional information network big data mining algorithm based on the finite element analysis method. This paper firstly introduces the finite element analysis and calculation process, a finite element data mining simulation application software management system will integrate current data, calculation, and background data into one, then analyzes the data mining clustering algorithm, and conducts an experimental exploration of the influential node mining algorithm in complex networks. The experimental results show that the LIC algorithm is better than the CC algorithm, the DC algorithm, and the BC algorithm; its overall performance is improved by 30%, and the effect is better. The LIC algorithm can effectively and quickly determine the influential nodes, which is helpful for social network analysis.

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Salmam, Fatima Zahra, Mohamed Fakir, and Rahhal Errattahi. "Prediction in OLAP Data Cubes." Journal of Information & Knowledge Management 15, no. 02 (2016): 1650022. http://dx.doi.org/10.1142/s0219649216500222.

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Online analytical processing (OLAP) provides tools to explore data cubes in order to extract the interesting information, it refers to techniques used to query, visualise and synthesise the multidimensional data. Nevertheless OLAP is limited on visualisation, structuring and exploring manually the data cubes. On the other side, data mining allows algorithms that offer automatic knowledge extraction, such as classification, explanation and prediction algorithms. However, OLAP is not capable of explaining and predicting events from existing data; therefore, it is possible to make a more efficient online analysis by coupling data mining and OLAP to allow the user to assist in this new task of knowledge extraction. In this paper, we will carry on within works achieved in this theme and we suggest to extend the abilities of OLAP to prediction (enhancing the OLAP abilities and techniques by introducing a predictive model based on a data mining algorithms). The model is calculated on the aggregated data, and prediction is done on detailed missing data. Our approach is based on regression trees and neural networks; it consists to predict facts having a missed measures value in the data cubes. The user will have in his disposition, a new platform called PredCube, that offers the possibility to query, visualise and synthesise the multidimensional data, and also to predict missing values in the data cube using three data mining methods, and evaluate the quality of the prediction by comparing the average error and the execution time given by each one.

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Jiang, He, Ai Xin Yang, and Hong Jun Yu. "Study on Multidimensional Negative Association Rules." Applied Mechanics and Materials 644-650 (September 2014): 1721–24. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1721.

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With the deepening of the negative association rules mining technology research, many key problems have been solved, but the solution of these problems are all on a single predicate in the transaction database. However, the data in the database often involves multiple predicates. This paper focuses on solving multi-dimensional support and confidence, negative association rules mining algorithm design problems. The experiment proves that the algorithm is correct and efficiency.

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Hira, Swati, and P. S. Deshpande. "Data Analysis using Multidimensional Modeling, Statistical Analysis and Data Mining on Agriculture Parameters." Procedia Computer Science 54 (2015): 431–39. http://dx.doi.org/10.1016/j.procs.2015.06.050.

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Rajawat, Anand Singh, Pradeep Bedi, S. B. Goyal, et al. "Dark Web Data Classification Using Neural Network." Computational Intelligence and Neuroscience 2022 (March 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/8393318.

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There are several issues associated with Dark Web Structural Patterns mining (including many redundant and irrelevant information), which increases the numerous types of cybercrime like illegal trade, forums, terrorist activity, and illegal online shopping. Understanding online criminal behavior is challenging because the data is available in a vast amount. To require an approach for learning the criminal behavior to check the recent request for improving the labeled data as a user profiling, Dark Web Structural Patterns mining in the case of multidimensional data sets gives uncertain results. Uncertain classification results cause a problem of not being able to predict user behavior. Since data of multidimensional nature has feature mixes, it has an adverse influence on classification. The data associated with Dark Web inundation has restricted us from giving the appropriate solution according to the need. In the research design, a Fusion NN (Neural network)-S3VM for Criminal Network activity prediction model is proposed based on the neural network; NN- S3VM can improve the prediction.

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Sucharittham, Nanthawadee, Choochart Haruechaiyasak, Hieu Chi Dam, and Thanaruk Theeramunkong. "Multidimensional Sentiment Cube Mining for Process Monitoring." Trends in Sciences 19, no. 9 (2022): 3682. http://dx.doi.org/10.48048/tis.2022.3682.

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Process monitoring is essential for quality improvement because it is necessary to find the answers to which business issues need to be understood. In the era of social media, many critiques concern the business domain, including life insurance, which is one of the significant business sectors in Thailand. To utilize this useful cloud corpus for the business improvement process, we propose a novel methodology for process monitoring using the concept of multidimensional sentiment cube (MDSC) mining to raise usefulness with the business process model notation (BPMN). As the ability of MDC raise unlimited analysis perspectives merge with sentiment analysis (MDSC), this method can provide more sets of data for association rules mining and meet the needs to be analyzed. The cube analysis scenario, which uses association rules mining results, can reveal a significant hidden issue among aspects and sub-aspects associated under our design with their measurements. The results can be used for monitoring, which presents the customer's sentiment from social media in the real business case and identifying in the real process model. HIGHLIGHTS  A methodology for process monitoring using the concept of multidimensional sentiment cube (MDSC) mining raise the usefulness of the business process model notation (BPMN) by utilizing the corpus for the business improvement process This method can provide more sets of data for association rules mining as the ability of MDC raise unlimited analysis perspectives to merge with sentiment analysis (MDSC) The results can monitor the customer’s sentiment from social media in the real business case and identify in the real process model  GRAPHICAL ABSTRACT

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Li, ZhenXin. "The Digital Library Archives Information Integration Based on Multidimensional Data Mining." International Journal of Reasoning-based Intelligent Systems 1, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijris.2023.10050992.

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Sankaradass. "A Descriptive Framework for the Multidimensional Medical Data Mining and Representation." Journal of Computer Science 7, no. 4 (2011): 519–25. http://dx.doi.org/10.3844/jcssp.2011.519.525.

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Si, Haiping, Changxia Sun, Hongbo Qiao, and Yanling Li. "Application of improved multidimensional spatial data mining algorithm in agricultural informationization." Journal of Intelligent & Fuzzy Systems 38, no. 2 (2020): 1359–69. http://dx.doi.org/10.3233/jifs-179499.

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Gonzalez, Hector, Jiawei Han, Yanfeng Ouyang, and Sebastian Seith. "Multidimensional Data Mining of Traffic Anomalies on Large-Scale Road Networks." Transportation Research Record: Journal of the Transportation Research Board 2215, no. 1 (2011): 75–84. http://dx.doi.org/10.3141/2215-08.

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Usman, Muhammad, Russel Pears, and A. C. M. Fong. "A data mining approach to knowledge discovery from multidimensional cube structures." Knowledge-Based Systems 40 (March 2013): 36–49. http://dx.doi.org/10.1016/j.knosys.2012.11.008.

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Hu, Jun, Jun Fang, Yanhua Du, Zhe Liu, and Pengyang Ji. "Application of PLS algorithm in discriminant analysis in multidimensional data mining." Journal of Supercomputing 75, no. 9 (2019): 6004–20. http://dx.doi.org/10.1007/s11227-019-02900-y.

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Jiang, He, Ze Bai, Guo Ling Liu, and Xiu Mei Luan. "An Algorithm for Mining Multidimensional Positive and Negative Association Rules." Advanced Materials Research 171-172 (December 2010): 445–49. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.445.

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Research on negative association rule in multidimensional data mining is few. In this paper, an algorithm MPNAR is put forward to mine positive and negative association rules in multidimensional data. With the help of the basis of the minimum support and minimum confidence, this algorithm divided the multidimensional datasets into infrequent itemsets and frequent itemsets. The negative association rules could be mined from infrequent itemsets. Relative to the single positive association rule mining, the new additional negative association rules need not repeatedly read database because two types of association rules were simultaneously mined. Experiments show that the algorithm method is effective and valuable.

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Solmaz, Mustafa, Adam Lane, Bilal Gonen, Ogulsheker Akmamedova, Mehmet H. Gunes, and Kakajan Komurov. "Graphical data mining of cancer mechanisms with SEMA." Bioinformatics 35, no. 21 (2019): 4413–18. http://dx.doi.org/10.1093/bioinformatics/btz303.

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Abstract Motivation An important goal of cancer genomics initiatives is to provide the research community with the resources for the unbiased query of cancer mechanisms. Several excellent web platforms have been developed to enable the visual analyses of molecular alterations in cancers from these datasets. However, there are few tools to allow the researchers to mine these resources for mechanisms of cancer processes and their functional interactions in an intuitive unbiased manner. Results To address this need, we developed SEMA, a web platform for building and testing of models of cancer mechanisms from large multidimensional cancer genomics datasets. Unlike the existing tools for the analyses and query of these resources, SEMA is explicitly designed to enable exploratory and confirmatory analyses of complex cancer mechanisms through a suite of intuitive visual and statistical functionalities. Here, we present a case study of the functional mechanisms of TP53-mediated tumor suppression in various cancers, using SEMA, and identify its role in the regulation of cell cycle progression, DNA repair and signal transduction in different cancers. SEMA is a first-in-its-class web application designed to allow visual data mining and hypothesis testing from the multidimensional cancer datasets. The web application, an extensive tutorial and several video screencasts with case studies are freely available for academic use at https://sema.research.cchmc.org/. Availability and implementation SEMA is freely available at https://sema.research.cchmc.org. The web site also contains a detailed Tutorial (also in Supplementary Information), and a link to the YouTube channel for video screencasts of analyses, including the analyses presented here. The Shiny and JavaScript source codes have been deposited to GitHub: https://github.com/msolmazm/sema. Supplementary information Supplementary data are available at Bioinformatics online.

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Ding, Heng, Qian Yuan Zhang, Yan Ping Wang, and Yan Jiang. "Data Warehousing and Data Mining Technology Implementation in Jpeen_CRM System Design." Applied Mechanics and Materials 610 (August 2014): 769–74. http://dx.doi.org/10.4028/www.scientific.net/amm.610.769.

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In this paper, on the basis of in-depth study of data warehouse, OLAP, data mining and other key technologies, according to the characteristics of the Jpeen customer relationship management (Jpeen_CRM), developed a Web-based customer relationship management system. First of all, research the demand for Jpeen company and create Jpeen_CRM data warehouse. Second, create the OLAP multidimensional cube which is applied to the analysis of customer transactions. Using the decision tree algorithm to create mining models for customers to choose glasses, and gives pruning optimize decision tree. Using decision tree to classify users and recommend them the hot products they prefer. Finally, realize the Jpeen_CRM on J2EE platform, provide a decision support for Jpeen company to better serve customers and make the company itself have the advantages in a competitive environment.

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Kim, Jiyun, and Han-joon Kim. "Multidimensional Text Warehousing for Automated Text Classification." Journal of Information Technology Research 11, no. 2 (2018): 168–83. http://dx.doi.org/10.4018/jitr.2018040110.

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This article describes how, in the era of big data, a data warehouse is an integrated multidimensional database that provides the basis for the decision making required to establish crucial business strategies. Efficient, effective analysis requires a data organization system that integrates and manages data of various dimensions. However, conventional data warehousing techniques do not consider the various data manipulation operations required for data-mining activities. With the current explosion of text data, much research has examined text (or document) repositories to support text mining and document retrieval. Therefore, this article presents a method of developing a text warehouse that provides a machine-learning-based text classification service. The document is represented as a term-by-concept matrix using a 3rd-order tensor-based textual representation model, which emphasizes the meaning of words occurring in the document. As a result, the proposed text warehouse makes it possible to develop a semantic Naïve Bayes text classifier only by executing appropriate SQL statements.

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Alsarayrah, Jameel, and AlaaTawfiq AL-Zyadat. "Multidimensional Small Medium Enterprises Achievement Rating : Improving to Data Warehousing & Data Mining Methods." International Journal of Information Technology Convergence and Services 8, no. 1/2 (2018): 01–08. http://dx.doi.org/10.5121/ijitcs.2018.8201.

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Wang, Hairong, Pan Huang, and Xu Chen. "Research and Application of a Multidimensional Association Rules Mining Method Based on OLAP." International Journal of Information Technology and Web Engineering 16, no. 1 (2021): 75–94. http://dx.doi.org/10.4018/ijitwe.2021010104.

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As to the problems of low data mining efficiency, less dimensionality, and low accuracy of traditional multidimensional association rules in the university big data environment, an OLAP-based multi-dimensional association rule mining method is proposed, which combines hash function and marked transaction compression technology to solve the problem of excessive or redundant candidate sets in the Apriori algorithm, and uses On Line Analytical Processing to manage the intermediate data in the association mining process , in order to reduce the time overhead caused by repeated calculations. To verify the validity of the proposed method, a learning situation analysis system is constructed in the field of colleges and universities. The multi-dimensional association rules mining method is used to analyze more than 21,000 desensitized real data, in order to mine the key factors affecting students' academic performance. The experimental results show that the proposed multi-dimensional mining model has good mining results and significantly improves the time performance.

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Yang, Wanzhong, Yuefeng Li, Jingtong Wu, and Yue Xu. "Granule mining oriented data warehousing model for representations of multidimensional association rules." International Journal of Intelligent Information and Database Systems 2, no. 1 (2008): 125. http://dx.doi.org/10.1504/ijiids.2008.017248.

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Articles de revues sur le sujet « Multidimensional data mining »

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