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Prototype vector mi where i = {4, 5, 6} cannot be BMU for xi, as d (m, mi) ≥ 2 · d (xi, m). Thus, these prototype vectors can be ignored in finding BMU.
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Triangle
inequality
optimization
is one of several strategies on the \(k\)-means algorithm that can reduce the search space in finding the nearest prototype vector. This optimization can also be applied towards Self-Organizing Maps training, particularly during finding the best matching unit in the batch training approach. This paper investigates v...
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