Normalization possibilistic fuzzy entropy clustering method based on Gaussian kernel hybrid artificial bee colony algorithm

Abstract

The invention relates to a normalization possibilistic fuzzy entropy clustering method based on a Gaussian kernel hybrid artificial bee colony algorithm. The method comprises: (1), carrying out normalization pretreatment to obtain a new sample set X_new; (2), carrying out parameter initialization; (3), carrying out calculation to obtain a distance to an initial clustering center and carrying out calculation on a membership matrix U and a possibilistic matrix T to obtain an initial fitness value fitness (i); (4), entering a honey gatering bee stage; (5), entering a following bee stage; (6), entering a scout bee stage; and (7), obtaining a final optimal clustering center Vbest, obtaining a corresponding membership matrix U by the Vbest, and then obtaining a final clustering unit according to a formula: ci=argmax(uij). The provided method has the great noise robustness; the human dependence of parameters is reduced to a certain extent; and after artificial bee colony algorithm introduction, the global characteristic of the algorithm is improved and a parameter initial value sensitivity problem is solved. The feasibility and effectiveness are improved.

Description

technical field [0001] The invention relates to a normalized possibility fuzzy entropy clustering method based on a Gaussian kernel mixed artificial bee colony algorithm, belonging to the technical fields of big data mining and machine learning. Background technique [0002] Fuzzy clustering analysis is an important means of unsupervised analysis of data, understanding data, and cognition of things. Due to the introduction of fuzzy sets and fuzzy mathematics, the uncertainty description between sample data and categories is established through the membership function, effectively It solves the clustering problem that is imprecise and has no obvious boundary "this and that" in reality. Fuzzy clustering has good data expression ability and clustering effect, and has been successfully applied to real-time cluster analysis, pattern classification, risk trend prediction, and decision analysis of massive data, providing people with in-depth understanding of data, deep utilization ...

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Problems solved by technology

[0005] The present invention solves the impact of dimension inconsistency on the clustering results by normalizing the original sample data; in addition, the present invention introduces a Gaussian kernel function to map the data of the original sample space to a high-dimensional feature space, solving the problem of The problem of unstable clustering of high-dimensional, non-convex, non-linear separable structure data; finally, the present invention also introduces an artificial bee colony algorithm with a unique global optimization capability, which optimizes and improves the global optimization characteristics of the algorithm

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