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AUTHOR(S): Osamah Mashaqbeh, Khaled Batiha, Wafa Alsharafat
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TITLE Two-Level Clustering Hierarchies using Fuzzy clustering in Wireless Sensor Networks |
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ABSTRACT Fuzzy algorithms are highly regarded for their simplicity, efficiency, and rapid implementation. They play a crucial role in clustering and classification tasks, as seen with C-means and K-means algorithms. In this paper, we introduced a two-level clustering hierarchy that uses fuzzy clustering techniques. The proposed work proved more effective than well-established methods such as K-means and C-means in handling nonlinear network clusters. We evaluated our proposed work using iris datasets and randomly generated datasets on a multicore system. The outcomes demonstrated that our research techniques yielded adequate performance results for both datasets compared to the K-means and C-means methods. |
KEYWORDS Fuzzy clustering, Head cluster, iris, Wireless Sensor Network |
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Cite this paper Osamah Mashaqbeh, Khaled Batiha, Wafa Alsharafat. (2024) Two-Level Clustering Hierarchies using Fuzzy clustering in Wireless Sensor Networks. International Journal of Computers, 9, 22-26 |
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