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K-means clustering problem details
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K-means algorithm is hard clustering algorithm, is a typical representative of the objective function cluster method based on prototypes, it is the data points to the prototype of a certain distance as the optimization of the objective function, using the method of extreme value of function be iterative adjustment rules. K-means clustering algorithm based on Euclidean distance as the similarity measure, it is a prayer that corresponds to an initial cluster centers vector v-optimal classification, j evaluation indicators the minimum. Algorithm uses squared error criterion function as clustering criteria. Using k-means clustering the target point is given a detailed code.
xiaoqi007 1 0
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Detailed explanation of genetic algorithms for sol
4.0
Genetic algorithm (Genetic Algorithm) is a simulated Darwinian evolution natural selection and calculation model of the genetic mechanisms of evolution, is a way to search for optimal solutions by mimicking natural evolution process. Legacy algorithms for solving extreme value problems, this paper provides a detailed code for reference.
xiaoqi007 1 0
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Track k-means clustering algorithm
4.0
Using MATLAB to develop programming, identification and extraction of track turning points, and then using the k-means algorithm for clustering will track turning points are identified in clustering, track turning points are divided into several categories, which use the MATLAB system comes with UTM coordinates conversion function, is about to convert the geodetic coordinates to plane coordinates, and read files in the database.
xiaoqi007 1 0
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