Hybrid clustering algorithm based on adaptive cellular inheritance and optimal fuzzy C-means

Abstract

The invention discloses a hybrid clustering algorithm based on adaptive cellular inheritance and optimal fuzzy C-means. The algorithm comprises the following steps that: utilizing Arnold Cat mapping to generate an initial population, and constructing a fitness function on the basis of the clustering criteria of the C-means; decoding individuals in the population to obtain a corresponding clustering center, distributing a degree of membership, and calculating a fitness value and the entropy of the population; carrying out state evolution on each individual, carrying out selection, dynamic intersection and a combined variation operation based on the entropy; automatically determining the fusion opportunity of the fuzzy C-means, and utilizing an implementation criteria to carry out a fuzzy C-means iterative operation; and judging whether an end condition is achieved or not, and outputting a final clustering result if the end condition is met. By use of the algorithm, the characteristics of the high global search capability of an adaptive cellular genetic algorithm and the high local search capability of a fuzzy C-means algorithm are further utilized. Compared with the prior art, the algorithm is characterized in that higher clustering efficiency and accuracy can be obtained.

Description

technical field [0001] The invention relates to a fuzzy clustering method, in particular to a hybrid clustering algorithm based on self-adaptive cell genetics and optimal fuzzy C-means. Background technique [0002] With the rapid development of computer and storage technology, the amount of data available to people is increasing exponentially. In the face of massive data, how to use computers to automatically classify data into different categories according to certain topics and extract useful and highly relevant knowledge has become increasingly important. Therefore, clustering technology is widely used in many fields such as data mining, machine recognition, image segmentation, fault diagnosis and pattern recognition. As an important method to find the natural aggregation structure of data, cluster analysis is mainly divided into hard clustering and fuzzy clustering. The hard clustering algorithm is simple and less time-consuming, but it is not suitable for dealing wit...

AI Technical Summary

Problems solved by technology

However, because the cellular genetic algorithm limits the positional relationship between individuals, the information exchange between individuals is also limited within their neighborhood, which reduces the efficiency of optimization search, so the optimization speed is generally slow.

The fuzzy C-means converges quickly, but it is easy to fall into local extremum

Images