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- W4313227069 abstract "Unsupervised machine learning includes data clustering, which discovers and comprehends underlying data patterns as well as classifies objects within a dataset based on similarity metrics such as Davies-Bouldin (DB) and CompactSeparatedValues (CSV) (CS). The most well-known and powerful partitional clustering algorithm is the K-means clustering algorithm, which is one of the top ten most frequently used data mining algorithms. We have combined the firefly metaheuristic optimization algorithm with K-means clustering to enhance the K-means for automatic clustering in our paper. The experimental results show that the hybridized algorithm KFA outperforms the k-means algorithm than PSO, KPSO, and K-means in terms of efficiency." @default.
- W4313227069 created "2023-01-06" @default.
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- W4313227069 date "2022-10-10" @default.
- W4313227069 modified "2023-09-27" @default.
- W4313227069 title "Automatic Clustering for Selection of Optimal Number of Clusters by K-Means Integrated with Enhanced Firefly Algorithms" @default.
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- W4313227069 doi "https://doi.org/10.1109/ictacs56270.2022.9988619" @default.
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