Acceleration and Clustering of Liver Disorder Using K-Means Clustering Method with Mahout’s Library
Abstract
Evaluation of liver disorders was performed to observed and clustered in Big Data environment applications. However, since liver disorder is a common illness, global awareness of such cases can be life threatening, therefore the urge to avoid and study must be essential. The idea of parallel computing is established on the basis of the K-means method. The MapReduce framework is used to complete multi-node data processing, and a solution to the MapReduce K-Means method is given. The ultimate goal is to establish clusters that allow each entity to be examined and assigned to a certain cluster. These algorithms are designed to accelerate computations, reduce the volume of enormous data that must be computed, and improve the efficiency of arithmetic operations. The combination of theoretical analysis and experimental evaluation is very significant.
Downloads
References
Sadhasivam, "Liver disease prediction using machine learning classification," Webology 18.Special Issue on Information Retrieval and Web Search, pp. 441-452, 2021. doi: 10.14704/WEB/V18SI02/WEB18293
Markelle Kelly, "University of California Irvine," 2023. [Online]. Available: https://archive.ics.uci.edu.
V. R. Eluri, A comparative study of various clustering techniques on big data sets using Apache Mahout, Muscat: IEEE, 2016. doi: 10.1109/ICBDSC.2016.7460397
Rokach. L., Data mining and knowledge discovery handbook, Berlin: Germany: Springer, 2010, pp. 22-32. [Online] Available: https://link.springer.com/chapter/10.1007/978-0-387-09823-4_1
Na, "Research on k-means clustering algorithm: An improved k-means clustering algorithm," in 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, Jian, China, 2010. doi: 10.1109/IITSI.2010.74
Dayong, Research on Supply Chain Management Strategy of Longtang Electric Engineering Co. Ltd, Kuala Lumpur: Acta Electronica Malaysia, 2019. doi: 10.26480/aem.01.2019.10.13
Meisam D., Webometrics Analysis of Iranian Universities about Medical Sciences’ Websites between September 2016 AND March 2017, Kuala Lumpur: Acta Informatica Malaysia, 2019. doi: 10.26480/aim.01.2019.07.12
Ou Z., A Look at Millennial Attitudes Toward AI Utility in The Class., Kuala Lumpur: Information Management and Computer Science, 2019. doi: 10.26480/imcs.01.2019.07.09
Prasetyo, "Comparison of distance and dissimilarity measures for clustering data with mix attribute types," in 2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering, jakarta, 2014. doi: 10.1109/ICITACEE.2014.7065756
Ahmed, "The k-means algorithm," A comprehensive survey and performance evaluation., p. Electronics 9.8: 1295, 2020. [Online] Available: https://doi.org/10.3390/electronics9081295
Sinaga, "Unsupervised K-means clustering algorithm.," Yogyakarta, IEEE access 8, 2020, pp. 80716-80727. doi: 10.1109/ACCESS.2020.2988796
Copyright (c) 2023 Journal of Systems Engineering and Information Technology (JOSEIT)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
