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Nandagopal, S.
- Improved Association Rule Modelling Using Various Machine Learning Modules for Large Datasets
Abstract Views :71 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science and Engineering, Nandha College of Technology, IN
1 Department of Computer Science and Engineering, Nandha College of Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 2 (2023), Pagination: 2904-2908Abstract
There are four modules namely Modified Apriori Algorithm (MAA), Crumb Based Association Rule Mining (CBARM), Inter-transaction Association Rule (IAR) miner and Categorized and Bounded Inter-Transaction (CBIT) proposed in this research work. The methodology of data mining is a relatively new field of study that has grown over the course of several decades of research and practise, drawing on the findings made in a wide variety of other fields of study. The reality that data mining studies and implementations are exceedingly difficult cannot be avoided in any manner. The development of data mining follows a process that is analogous to the development of any other new technology. This process begins with the presentation of an idea and is then followed by stages in which the concept is accepted, major research and exploration is conducted, incremental application is performed, and finally mass deployment occurs. The great majority of researchers working in the academic world are of the opinion that the process of data mining is still in its infancy in terms of both research and investigation.Keywords
Association Rule, Machine Learning, Rule Mining
References
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- I.H. Sarker and A. Ng, “Cybersecurity Data Science: An Overview from Machine Learning Perspective”, Journal of Big data, Vol. 7, pp. 1-29, 2020.
- X. Zhou and Q. Jin, “Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things”, IEEE Internet of Things Journal, Vol. 7, No. 7, pp. 6429-6438, 2020.
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- Z. Wu, J. Cao and Y. Ge, On Scalability of Association-Rule-based Recommendation: A Unified Distributed-Computing Framework”, ACM Transactions on the Web (TWEB), Vol. 14, No. 3, pp. 1-21, 2020.
- J. Surendiran, S. Theetchenya and M. Dhipa, “Segmentation of Optic Disc and Cup Using Modified Recurrent Neural Network”, BioMed Research International, Vol. 2022, pp. 1-13, 2022.
- I. Ullah and S.W. Kim, “A Churn Prediction Model using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector”, IEEE Access, Vol. 7, pp. 60134-60149, 2019.
- A. Telikani and A. Shahbahrami, “A Survey of Evolutionary Computation for Association Rule Mining”, Information Sciences, Vol. 524, pp. 318-352, 2020.
- P. Ghavami, “Big Data Analytics Methods: Analytics Techniques in Data Mining, Deep Learning and Natural Language Processing”, Walter de Gruyter, 2019.
- I. Lee and Y.J. Shin, “Machine Learning for Enterprises: Applications, Algorithm Selection, and Challenges”, Business Horizons, Vol. 63, No. 2, pp. 157-170, 2020.
- Y. Mourdi and W. Berrada Fathi, “A Machine Learning-based Methodology to Predict Learners’ Dropout, Success or Failure in MOOCs”, International Journal of Web Information Systems, Vol. 15, No. 5, pp. 489-509, 2019.
- S. Neelakandan and D. Paulraj, “An Automated Exploring and Learning Model for Data Prediction using balanced CA-SVM”, Journal of Ambient Intelligence and Humanized Computing, Vol. 12, pp. 4979-4990, 2021.
- Deep Association Rule-based Dimensionality Reduction for Big Data Analytics Uncovering Complex Patterns and Improving Efficiency
Abstract Views :21 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Nandha College of Technology, IN
1 Department of Computer Science and Engineering, Nandha College of Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 4 (2023), Pagination: 3061-3067Abstract
In the realm of big data analytics, reducing data dimensionality is crucial for efficient processing and gaining meaningful insights. This paper introduces a novel approach that leverages deep association rule mining to uncover intricate patterns and associations within large datasets. By incorporating deep learning models, such as neural networks, our method captures complex and non-linear relationships that traditional techniques often overlook. The proposed framework involves preprocessing the data, creating embedding representations, training a deep learning model, extracting association rules, filtering and selecting relevant rules, and applying dimensionality reduction. The selected association rules serve as the basis for reducing the dimensionality of the original dataset, either by removing irrelevant variables or consolidating them into higher-level features. The efficacy of our approach is evaluated through experiments, showcasing improved efficiency and the preservation of meaningful information. This research presents a promising avenue for reducing data dimensionality in big data analytics, enhancing the scalability and interpretability of analysis outcomes.Keywords
Big Data Analytics, Dimensionality Reduction, Deep Association Rule Mining, Deep Learning, Neural Networks.References
- G.T. Reddy and T. Baker, “Analysis of Dimensionality Reduction Techniques on Big Data”, IEEE Access, Vol. 8, pp. 54776-54788, 2020.
- Z. Zhao and S. Rubaiee, “An Improved Association Rule Mining Algorithm for Large Data”, Journal of Intelligent Systems, Vol. 30, No. 1, pp. 750-762, 2021.
- W. Ma, W. Cai and Y. Liu, “Deep Learning for the Design of Photonic Structures”, Nature Photonics, Vol. 15, No. 2, pp. 77-90, 2021.
- J.B. Awotunde, “Intrusion Detection in Industrial Internet of Things Network-based on Deep Learning Model with Rule-based Feature Selection”, Wireless communications and mobile computing, Vol. 89, pp. 1-17, 2021.
- C. Zhang and Z. Yin, “An Association Rule based Approach to Reducing Visual Clutter in Parallel Sets”, Visual Informatics, Vol. 3, No. 1, pp. 48-57, 2019.
- M.Q. Bashabsheh, “Big Data Analysis using Hybrid Meta-Heuristic Optimization Algorithm and Map Reduce Framework”, Proceedings of International Conference on Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems, pp. 181-223, 2022.
- M.I. Razzak and G. Xu, “Big Data Analytics for Preventive Medicine”, Neural Computing and Applications, Vol. 32, pp.4417-4451, 2020.
- Y. Himeur and A. Amira, “AI-Big Data Analytics for Building Automation and Management Systems: A Survey, Actual Challenges and Future Perspectives”, Artificial Intelligence Review, Vol. 56, No. 6, pp. 4929-5021, 2023.
- Y. Himeur and A. Amira, “AI-Big Data Analytics for Building Automation and Management Systems: A Survey, Actual Challenges and Future Perspectives”, Artificial Intelligence Review, Vol. 56, No. 6, pp. 4929-5021, 2023.
- I.H. Sarker, H. Alqahtani, P. Watters and A. Ng, “Cybersecurity Data Science: An Overview from Machine Learning Perspective”, Journal of Big Data, Vol. 7, pp. 1-29, 2020.