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For more features massive data sets efficiently reduction problems, to enhance the data uniformity of the near target instance, reduce the errors of represented neighbors, improve the effect of dimension reduction, proposed a dynamic K value method of massive data reduction based on improved LLE algorithm. Utilized the search algorithms based center point, searched an example neighborly, to ensure the neighbor instances which were distributed around the instance maximally and uniformly; to guarantee the change of K value accorded with the characteristics of the non-uniform data sets, put forward the method to adjust the K value dynamically according to the local uniformity changes of data sets, designed the changing rules of K value to meet the need of algorithm dimensionality reduction for the non-uniform data sets. The experimental results shown, utilized this improved method to carry out dimensionality reduction and classification for the non-uniform data sets in this thesis, and has certain advantages compared with other methods.

A Dynamic K Value Method of Massive Data Reduction Based on Improved LLE Algorithm