Enhancing Clustering Accuracy Using K-Means with Seeds Optimization
DOI:
https://doi.org/10.30871/jaic.v9i5.10458Keywords:
Clustering, Data Mining, Machine Learning, Health, HeredityAbstract
In this study, the development of the Mean-based method proposed by Goyal and Kumar will be carried out by changing the initial cluster center determination step, which was originally based on the origin point O (0,0), to be replaced with the arithmetic mean. To assess the performance of the proposed method, it will be compared with the Global K-means method and the Mean-based K-means method. In this study, the performance of these methods will be measured using the Davies-Bouldin Index, and the significance of the proposed method will be measured using the Friedman Test. This study proposes a method of Improving K-Means Performance through Initial Center Optimization based on Second Global Average for Clustering Osteoporosis Diagnosis of lifestyle factors. Evaluation of K-Means performance through Initial Center Optimization based on Second Global Average with DBI measurements. The targeted experimental results of this study include improving the performance of K-means optimized through the initial center based on Second Global Average. From the results of nine experiments with the number of clusters [2,3,4,5,6], it can be seen that the method proposed in this study has the same superior performance compared to the Mean Based method and compared to the Global K-means method.
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Copyright (c) 2025 Adiyah Mahiruna, Ngatimin Ngatimin, Rachmat Destriana, Eko Hari Rachmawanto, Herman Yuliansyah, Muhammad Taufiq Hidayat
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