Characteristic vector group's best selected spectrum clustering method based on density self-adaptation

Abstract

The invention proposes a characteristic vector group's best selected spectrum clustering method based on density self-adaptation. The method comprises the following steps: 1) first, performing pre-treatment; 2) calculating the sparse similarity matrix based on density self-adaptation; 3) calling the automatically determining clustering central algorithm; 4) decomposing the characteristics and seeking the characteristic vector group; 5) performing the standardizing treatment to all elements in the data set in the mapped characteristic vector group in the characteristic space, followed by the K-means clustering to obtain the clustering result; and 6) calculating the Fitness function values; performing iterations constantly; and selecting and outputting the clustering result of the number of the best clear-cut points corresponding to the highest Fitness function value. According to the invention, it is possible to introduce the data point density information to a similar function so as to improve the time gathering effect of multi-scale data of the algorithm processing, to select the best characteristic vector group and to utilize the Fitness function to realize the parameter self-adaptation of the number of clear-cut points.

Description

technical field [0001] The invention belongs to the field of spectral clustering algorithms, in particular to a spectral clustering method for optimal selection of feature vector groups based on density self-adaptation. Background technique [0002] Clustering is one of the important research contents in data mining, pattern recognition, machine learning and other fields. Clustering is to gather samples with higher similarity together and put samples with lower similarity in different clusters. With the development of clustering technology, scholars have proposed a variety of clustering algorithms, such as the most classic K-means algorithm in cluster analysis, which is relatively simple and can be used for clustering of various types of data. But when dealing with non-convex data sets, the K-means algorithm tends to fall into a local optimal solution. In order to solve the problem that K-means algorithm cannot cluster on non-convex data sets, researchers began to use spec...

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Problems solved by technology

[0005] In order to overcome the existing spectral clustering methods, some custom parameters need to be set manually, the effect is not ideal when dealing with multi-scale data sets, the number of clusters needs to be manua...

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