2009 International Conference on Information Technology and Computer Science 2009
DOI: 10.1109/itcs.2009.299
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Cited by 5 publications
(1 citation statement)
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“…The results of this test are compared with a centralized benchmark (e.g., K-Means). The K-Means is primarily used in resource constrained environments due to its low complexity and fast execution in large data sets and it has been widely used in wireless sensor networks (e.g., [16,23,30,34,40,56,74,85]). According to [30], the general idea of the K-Mean clustering can be described as follows: Perhaps the most interesting finding is that the three solutions produced partitions…”
Section: Quality Of the Solutionmentioning
confidence: 99%
“…The results of this test are compared with a centralized benchmark (e.g., K-Means). The K-Means is primarily used in resource constrained environments due to its low complexity and fast execution in large data sets and it has been widely used in wireless sensor networks (e.g., [16,23,30,34,40,56,74,85]). According to [30], the general idea of the K-Mean clustering can be described as follows: Perhaps the most interesting finding is that the three solutions produced partitions…”
Section: Quality Of the Solutionmentioning
confidence: 99%