Data mining improved type K mean value clustering method based on linear discriminant analysis

Abstract

The invention relates to a data mining improved type K mean value clustering method based on linear discriminant analysis, namely an LKM algorithm. Firstly, an LDA is adopted to conduct linear dimensionality reduction on an original n-dimensional data set A to obtain a one-dimensional data set Y, then a k mean value clustering algorithm is adopted to conduct clustering analysis on the data set Y after dimensionality reduction, and final results are output. The method that data dimensionality reduction is combined with the K mean value clustering method is adopted, and defects of the k mean value clustering algorithm on high-dimensional data are overcome through the data dimension reduction technology. The aims of lightening dimensionality curses and eliminating other uncorrelated attributes in high-dimensional space are achieved through data dimension reduction. Meanwhile, the performance of the k mean value clustering algorithm for processing the high-dimensional data is also improved, and the correlated defects of the k mean value clustering algorithm are overcome.

Description

technical field [0001] The present invention is an optimization method based on linear discriminant analysis (LDA) to improve the performance of the K-means clustering method, that is, the LKM (LDA-based K-Means algorithm) algorithm, which belongs to the cluster analysis in data mining Research areas. Background technique [0002] Cluster analysis is an important research field in data mining, and it is an important means and method for data division or grouping. The current clustering algorithms are generally divided into partition-based methods, hierarchical-based methods, density-based methods, grid-based methods, model-based methods and fuzzy clustering. K-means clustering method is a typical clustering algorithm based on distance division, which uses distance as the evaluation index of similarity, that is, the closer the distance between two objects, the greater the similarity. Because of its simple algorithm idea and easy realization of large-scale data clustering, K...

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Problems solved by technology

[0006] Technical problem: The present invention aims at the problems that the K-means clustering method cannot perform cluster analysis on high-dimensional data, and cannot quickly process high-dimensional data by the K-means clustering method, and provides an improvement based on linear discriminant analysis in data mining The K-means clustering method uses the linear mapping of linear discriminant analysis to map the original high-dimensional data to the low-dimensional space one by one, completes the linear dimensionality reduction operation, and obtains low-dimensional data suitable for K-means clustering analysis, and completes Cluster analysis

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