Membrane computing frame-based spectral clustering algorithm

Abstract

The invention provides a membrane computing frame-based spectral clustering algorithm, which comprises the steps of constructing a similarity graph G; generating a laplacian matrix; figuring out feature vectors corresponding to the first k minimum feature values of the laplacian matrix, and constructing a feature vector space; clustering the feature vectors in the feature vector space by using themembrane clustering algorithm; realizing the membrane clustering purpose by adopting a tissue type P system, wherein the tissue type P system comprises q cells, each cell comprises m objects, and each cell is used for transferring an optimal object thereof to the environment by utilizing the transferring rule; updating the optimal object corresponding to the environment; adopting a PSO speed-displacement model as an evolutionary rule, and adopting the optimal object in the environment as an obtained optimal solution after the shutdown according to a preset shutdown condition. The membrane computing can be used for processing global optimization problems. Therefore, the membrane computing frame-based spectral clustering algorithm not only enriches the type of the clustering algorithm, butalso optimizes the effect of spectral clustering. The application field of membrane computing is expanded.

Description

technical field [0001] The invention belongs to the technical field of data mining and machine learning, and in particular relates to a spectral clustering algorithm based on a membrane computing framework. Background technique [0002] Cluster analysis is an important branch in the field of machine learning, and it is an effective means for people to understand and explore the inner relationship between mistakes. The so-called clustering (clustering) is to group data objects into multiple classes or clusters (cluster), so that objects in the same cluster have a high degree of similarity, while objects in different clusters are quite different. Among the existing clustering methods, k-means clustering, as a center-based clustering method, is the simplest and one of the most commonly used methods. Good performance, but when the data structure is non-convex, or the data points overlap each other seriously, the k-means clustering algorithm often fails, and the k-means has the ...

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Problems solved by technology

[0004] In view of this, the technical problem to be solved by the present invention is to provide a spectral clustering algorithm based on a membrane computing framework to solve the problem that the spectral clustering algorithm has a large gap in data set density and poor clustering effect in engineering applications. obvious defects

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