Impact and Challenges of Data Mining : A Comprehensive Analysis
DOI:
https://doi.org/10.32628/CSEIT241049Keywords:
Data Mining, Data Warehouses, Artificial Neural Networks, Decision Trees, Genetic Algorithms, Rule Induction, Statistical Analysis, Data Visualization, Iterative ProcessAbstract
This review paper provides a concise overview of Data Mining, a multidisciplinary field focused on extracting valuable insights and patterns from extensive datasets. It highlights the use of statistical analysis, machine learning, and pattern recognition techniques to discover hidden relationships and trends within data. The paper emphasizes data mining's significance as a powerful technology that extracts predictive information from large databases, enabling businesses to prioritize crucial data. It showcases how data mining tools predict future trends, empowering proactive, knowledge-driven decision-making. Furthermore, it discusses the superiority of data mining over retrospective tools, offering automated, prospective analyses to resolve complex business questions efficiently. It uncovers hidden patterns and predictive information beyond human expectations. The core concepts of data mining encountered challenges, data analysis techniques, and their profound impact on various domains are also addressed in this paper. The proposed paper offers a comprehensive overview of data mining's importance, applications, and transformative potential in modern data-driven decision-making processes.
Downloads
References
Tan, P. N., Steinbach, M., & Kumar, V. (2016). Introduction to data mining. Pearson Education India.
Larose, D. T. (2005). An introduction to data mining. Traduction et adaptation de Thierry Vallaud.
Phyu, T. N. (2009, March). Survey of classification techniques in data mining. In Proceedings of the international multiconference of Engineers and computer scientists (Vol. 1, No. 5, pp. 727-731). Citeseer.
Nikam, S. S. (2015). A comparative study of classification techniques in data mining algorithms. Oriental Journal of Computer Science and Technology, 8(1), 13-19.
Zhang, S., Zhang, C., & Yang, Q. (2003). Data preparation for data mining. Applied artificial intelligence, 17(5-6), 375-381. DOI: https://doi.org/10.1080/713827180
Introduction to Data Mining and Knowledge Discovery, Third Edition
Data Mining ’99: Technology Report, Two Crows Corporation, 1999
META Group Application Development Strategies: "Data Mining for Data Warehouses: Uncovering Hidden Patterns
https://www.matillion.com/resources/5-data-mining-business-intelligence-examples
Kriegel, H. P., Borgwardt, K. M., Kröger, P., Pryakhin, A., Schubert, M., & Zimek, A. (2007). Future trends in data mining. Data Mining and Knowledge Discovery, 15, 87-97.
Brown, M. L., & Kros, J. F. (2003). Data mining and the impact of missing data. Industrial Management & Data Systems, 103(8), 611-621. DOI: https://doi.org/10.1108/02635570310497657
Kriegel, H. P., Borgwardt, K. M., Kröger, P., Pryakhin, A., Schubert, M., & Zimek, A. (2007). Future trends in data mining. Data Mining and Knowledge Discovery, 15, 87-97. DOI: https://doi.org/10.1007/s10618-007-0067-9
Chen, S. Y., & Liu, X. (2004). The contribution of data mining to information science. Journal of Information Science, 30(6), 550-558. DOI: https://doi.org/10.1177/0165551504047928
Azzalini, A., & Scarpa, B. (2012). Data analysis and data mining: An introduction. OUP USA.
Grossman, R., Kasif, S., Moore, R., Rocke, D., & Ullman, J. (1999, January). Data mining research: opportunities and challenges.
Lakshmi, B. N., & Raghunandhan, G. H. (2011, February). A conceptual overview of data mining. In 2011 National Conference on Innovations in Emerging Technology (pp. 27-32). IEEE. DOI: https://doi.org/10.1109/NCOIET.2011.5738828
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
