2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 13 , Issue 1 , PP: 122-134, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Vibha Tiwari 1 * , Chopparapu Gowthami 2 , Bhavani R. 3 , S. Kayalvizhi 4 , S. Selvakanmani 5 , Deepak Chowdary Edara 6
Doi: https://doi.org/10.54216/JISIoT.130110
This manuscript proposes Strategic Improved K-Means Clustering to simplify blood donor data analysis and distribution. The technique optimizes blood donor system resources via K-Means++ initialization, hierarchical clustering, and smart data dissemination. The paper begins with a comprehensive overview of clustering techniques and their healthcare applications. It illustrates the need for contemporary blood donor data analysis methods for cluster quality and resource allocation. Cluster purity, silhouette coefficient, Davies-Bould in the index, and other performance indicators are used to rigorously compare the recommended technique to 10 established clustering methods. The approach routinely fulfils these conditions, proving that it creates accurate, well-fitting groupings. Ablation tests how much-enhanced initialization, hierarchical clustering, and strategic data placement improve the entire. The study found that these make the procedure dependable and successful for numerous sorts of data. The study shows that the approach may be applied to other data besides blood donor data. Hierarchical clustering provides important information about the dataset's hierarchical patterns, making clustering findings easier to grasp. Resources are better distributed with strategic data dissemination. The recommended strategy is effective in emergencies and areas with changing blood needs. To conclude, Strategic Improved K-Means Clustering evaluates and distributes blood donor data comprehensively. Its flexibility, adaptability, and speed make it excellent for managing healthcare resources and making collective choices.
Blood Donor , Clustering , Data Analysis , Healthcare , Hierarchical Clustering , K-Means++ , Optimization, Resource Allocation , IoT
[1] Y. Mei, J. Yu, J. Wang, and X. Li, "Data Analysis and Data Mining," Tsinghua University Press, Beijing, China, pp. 260-261, 2020.
[2] F. Wang and Z. Liu, "Optimization method of distributed K-means algorithm under Spark framework," Computer Engineering and Design, vol. 40, no. 06, pp. 1595–1600, 2019.
[3] Y. Zhang and F. Li, "Parallelization of canopy-K-means algorithm based on MapReduce," Journal of Liaoning University of Science and Technology, no. 01, pp. 4–13, 2017.
[4] D. Pathak and R. Kashyap, "Neural correlate-based E-learning validation and classification using convolutional and Long Short-Term Memory networks," Traitement du Signal, vol. 40, no. 4, pp. 1457-1467, 2023. [Online]. Available: https://doi.org/10.18280/ts.400414
[5] V. Roy, S. Shukla, P. K. Shukla, P. Rawat, "Gaussian Elimination-Based Novel Canonical Correlation Analysis Method for EEG Motion Artifact Removal", Journal of Healthcare Engineering, vol. 2017, Article ID 9674712, 11 pages, 2017. https://doi.org/10.1155/2017/9674712.
[6] S. Stalin, V. Roy, P. K. Shukla, A. Zaguia, M. M. Khan, P. K. Shukla, A. Jain, "A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach", Mathematical Problems in Engineering, vol. 2021, Article ID 2942808, 11 pages, 2021. https://doi.org/10.1155/2021/2942808
[7] Y. Liu, "Parallel K-means clustering algorithm based on sampling and maximum and minimum distance method," Intelligent Computer and Application, vol. 8, no. 06, pp. 37–39+43, 2018.
[8] Z. Wang, L. Jin, and Y. Song, "Improved K-means algorithm based on distance and weight," Computer Engineering and Applications, vol. 56, no. 23, pp. 87–94, 2020.
[9] C. Yan, "Data Mining Technology and Application," Tsinghua University Press, Beijing, China, p. 145, 2016.
[10] X. Li, L. Yu, and H. Lei, "Parallel implementation and application of an improved K-means algorithm," Journal of University of Electronic Science and Technology of China, vol. 46, no. 01, pp. 61–68, 2017.
[11] Roy V., Shukla S. (2013) Image Denoising by Data Adaptive and Non-Data Adaptive Transform Domain Denoising Method Using EEG Signal. In: Kumar V., Bhatele M. (eds) Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE 2012). Lecture Notes in Bioengineering. Springer, India. https://doi.org/10.1007/978-81-322-0970-6_2.
[12] V. Roy et al., “Detection of sleep apnea through heart rate signal using Convolutional Neural Network,” International Journal of Pharmaceutical Research, vol. 12, no. 4, pp. 4829-4836, Oct-Dec 2020.
[13] R. Kashyap, "Machine Learning, Data Mining for IoT-Based Systems," in Research Anthology on Machine Learning Techniques, Methods, and Applications, Information Resources Management Association, Ed. IGI Global, 2022, pp. 447-471. [Online]. Available: https://doi.org/10.4018/978-1-6684-6291-1.ch025
[14] Z. Tang, B. Huang, and L. Lian, "Collaborative filtering recommendation algorithm based on improved Canopy clustering," Computer Application Research, vol. 37, no. 09, pp. 2615–2619+2639, 2020.
[15] S. Chen and R. Jia, "Improved K-medoids algorithm based on density weight Canopy," Computer Engineering and Science, vol. 41, no. 10, pp. 1823–1828, 2019.
[16] F. Liu, Y. Juan, and L. Yao, "Research on the number of clusters in K-means clustering algorithm," Electronic Design Engineering, vol. 25, no. 15, pp. 9–13, 2017.
[17] J. Xie and Y. E. Wang, "K-Means algorithm for optimizing initial cluster centers with minimum variance," Computer Engineering, vol. 40, no. 08, pp. 205–211+223, 2014.
[18] H. P. Sahu and R. Kashyap, "FINE_DENSEIGANET: Automatic medical image classification in chest CT scan using Hybrid Deep Learning Framework," International Journal of Image and Graphics [Preprint], 2023. [Online]. Available: https://doi.org/10.1142/s0219467825500044
[19] S. Stalin, V. Roy, P. K. Shukla, A. Zaguia, M. M. Khan, P. K. Shukla, A. Jain, "A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach," Mathematical Problems in Engineering, vol. 2021, Article ID 2942808, 11 pages, 2021.
[20] J. Sun and S. Wang, "Data Mining Algorithms and Applications," Tsinghua University Press, Beijing, China, vol. 145, pp. 147–149, 2020.
[21] X. Jiang, W. Zhang, J. Wang, and L. Ma, "Improved SK-means strategy based on stream processing," Journal of Beijing Information Technology University (Natural Science Edition), vol. 36, no. 05, pp. 51–56, 2021.
