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Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets

Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets

Abha Sharma, Pushpendra Kumar, Kanojia Sindhuben Babulal, Ahmed J. Obaid, Harshita Patel
Copyright: © 2022 |Volume: 13 |Issue: 4 |Pages: 15
ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781668477557|DOI: 10.4018/IJEHMC.309440
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MLA

Sharma, Abha, et al. "Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets." IJEHMC vol.13, no.4 2022: pp.1-15. http://doi.org/10.4018/IJEHMC.309440

APA

Sharma, A., Kumar, P., Babulal, K. S., Obaid, A. J., & Patel, H. (2022). Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets. International Journal of E-Health and Medical Communications (IJEHMC), 13(4), 1-15. http://doi.org/10.4018/IJEHMC.309440

Chicago

Sharma, Abha, et al. "Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets," International Journal of E-Health and Medical Communications (IJEHMC) 13, no.4: 1-15. http://doi.org/10.4018/IJEHMC.309440

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Abstract

Healthcare analytics provide many benefits in healthcare dashboard systems. Healthcare datasets majorly contains categorical attributes. This paper proposed an optimized clustering for healthcare dataset named harmony search based categorical clustering (HSCC). The existing k-modes clustering algorithm is one of the well-known categorical data-clustering algorithm. Since the k-modes algorithm produces local optimal clusters. Generally, researchers use genetic algorithm (GA) based clustering algorithms to converge locally optimal solutions to global optimal solutions. GA has some deficiencies such as premature convergence with low speed. In this paper, harmony search (HS) optimization algorithm used to optimize clustering results. The result shows the proposed HSCC algorithm produced global optimized solution, unbiased and matured results. HSCC produces 98% accuracy for dental and 71% for lung cancer dataset. While GACC produces 95% and 65% accuracy for dental dataset and lung cancer dataset.