Selective clustering integration method based on data stability

Abstract

The invention discloses a selective clustering integration method based on data stability. The method comprises the following steps of 1) inputting a data set and carrying out preprocessing; 2) carrying out clustering result set generation on the data set; 3) carrying out clustering result screening and acquiring a clustering subset; 4) carrying out sample division, and dividing the data set intoa stable subset and an unstable subset; 5) making a target function based on the stable subset and the unstable subset, and further screening the clustering subset; and 6) fusing the final clusteringsubset and acquiring a clustering result. Compared with a traditional method, the method has the following innovation points that multi-view clustering is realized so as to enhance diversities; an appropriate clustering algorithm is automatically screened and a problem that a data assumption does not match is avoided; the target function based on data stability is designed and high adaptability isachieved; and through an index increase degree, a multi-target genetic algorithm convergence direction is controlled and a convergence speed and accuracy are increased.

Description

technical field [0001] The invention relates to the technical field of computer artificial intelligence, in particular to a selective clustering integration method based on data stability. Background technique [0002] Clustering analysis is an important and challenging problem in machine learning and data mining. The goal of clustering is to group similar samples into the same class, but different clustering algorithms have different assumptions about the data, and a single algorithm is difficult Handle complex feature representation problems. Clustering integration solves the above problems very well, so it is widely used. By fusing multiple clustering results with diversity and accuracy, the clustering effect can often be greatly improved, but there are many clustering results Noise members, if not removed, will affect the performance of clustering integration, the present invention mainly solves the problem of clustering integration selection. [0003] In traditional c...

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Problems solved by technology

[0005] The purpose of the present invention is to overcome the problems of lack of adaptability, dependence on prior knowledge, and low optimization efficiency in traditional clustering integration selection methods, and propose a selective clustering integration method based on data stability, which can effectively improve clustering diversity, can automatically identify the best clustering algorithm for a specific data set, has self-adaptability to the selection of clustering results, and is applicable to data sets with various characteristics, while the multi-objective genetic algorithm in the present invention has fast convergence speed, High precision, thus effectively improving the accuracy of cluster analysis

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