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Optimal Fuzzy Cluster Partitioning by Crow Search Meta-Heuristic for Biomedical Data Analysis

Optimal Fuzzy Cluster Partitioning by Crow Search Meta-Heuristic for Biomedical Data Analysis

Janmenjoy Nayak, Bighnaraj Naik, Pandit Byomakesha Dash, Danilo Pelusi
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 18
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799861133|DOI: 10.4018/IJAMC.2021040104
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MLA

Nayak, Janmenjoy, et al. "Optimal Fuzzy Cluster Partitioning by Crow Search Meta-Heuristic for Biomedical Data Analysis." IJAMC vol.12, no.2 2021: pp.49-66. http://doi.org/10.4018/IJAMC.2021040104

APA

Nayak, J., Naik, B., Dash, P. B., & Pelusi, D. (2021). Optimal Fuzzy Cluster Partitioning by Crow Search Meta-Heuristic for Biomedical Data Analysis. International Journal of Applied Metaheuristic Computing (IJAMC), 12(2), 49-66. http://doi.org/10.4018/IJAMC.2021040104

Chicago

Nayak, Janmenjoy, et al. "Optimal Fuzzy Cluster Partitioning by Crow Search Meta-Heuristic for Biomedical Data Analysis," International Journal of Applied Metaheuristic Computing (IJAMC) 12, no.2: 49-66. http://doi.org/10.4018/IJAMC.2021040104

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Abstract

Biomedical data is often more unstructured in nature, and biomedical data processing task is becoming more complex day by day. Thus, biomedical informatics requires competent data analysis and data mining techniques for designing decision support system's framework to solve clinical and heathcare-related issues. Due to increasingly large and complex data sets and demand of biomedical informatics research, researchers are attracted towards automated machine learning models. This paper is proposed to design an efficient machine learning model based on fuzzy c-means with meta-heuristic optimizations for biomedical data analysis and clustering. The main contributions of this paper are 1) projecting an efficient machine learning model based on fuzzy c-means and meta-heuristic optimization for biomedical data classification, 2) employing benchmark validation techniques and critical hypothesises testing, and 3) providing a background for biomedical data processing with a view of data processing and mining.

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