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Journal articles on the topic 'Multidimensional data mining'

Author: Grafiati

Published: 10 March 2023

Last updated: 31 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Namrata Kumari. "An analysis of Data mining techniques, multidimensional modelling on agriculture." Journal of Advances and Scholarly Researches in Allied Education 21, no. 3 (2024): 245–48. http://dx.doi.org/10.29070/f3sb8583.

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This articles analysis the “An analysis of Data mining techniques, multidimensional modelling on agriculture.” The sole method for using and obtaining knowledge from large data sets is data mining. Samples are taken in increments from various locations within a bag. A consignment can contain up to 30 bags, of which one must be selected at random from each bag. For consignments containing 50 bags, however, samples of the Magadh division blocks in Bihar must be obtained from each bag. additionally to investigate how changing weather affects agriculture and how data mining in government platforms

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12

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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13

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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14

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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15

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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16

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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17

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 cons

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18

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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19

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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20

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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21

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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22

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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23

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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24

Reszka, Łukasz, Janusz Sosnowski, and Bartosz Dobrzyński. "Enhancing Software Project Monitoring with Multidimensional Data Repository Mining." Electronics 12, no. 18 (2023): 3774. http://dx.doi.org/10.3390/electronics12183774.

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Software project development and maintenance activities have been reported in various repositories. The data contained in these repositories have been widely used in various studies on specific problems, e.g., predicting bug appearance, allocating issues to developers, and identifying duplicated issues. Developed analysis schemes are usually based on simplified data models while issue report details are neglected. Confronting this problem requires a deep and wide-ranging exploration of software repository contents adapted to their specificities, which differs significantly from classical data

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25

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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26

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 associa

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27

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 struct

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28

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 algor

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29

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 ba

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30

Lopata, Audrius, Daina Gudonienė, Rimantas Butleris, Ilona Veitaitė, Vytautas Rudžionis, and Saulius Gudas. "A Multidimensional Financial Data Model for User Interface with Process Mining Systems." Electronics 13, no. 21 (2024): 4304. http://dx.doi.org/10.3390/electronics13214304.

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Multidimensional enterprise performance characteristics (enterprise operational data, financial transactions records) are stored in the company’s database (warehouse), and their volume and variety are huge. Financial transaction data are directly and indirectly related to value chain processes, various physical objects of activity, and their attributes. There are data mining (DM) and process mining (PM) methods for analyzing enterprise operational data and identifying deficiencies in business process management. There is a need to find new user experience (UX)-driven methods for user interface

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31

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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32

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 efficien

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33

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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34

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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35

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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36

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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37

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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38

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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39

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.

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40

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 typ

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41

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

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42

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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43

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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44

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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45

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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46

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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47

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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48

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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49

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

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50

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 ver

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