Improved fuzzy C-mean clustering method based on quantum particle swarm optimization

Abstract

The invention relates to a clustering method, in particular relates to an improved fuzzy C-mean clustering method based on quantum particle swarm optimization, and belongs to the technical field of data mining and artificial intelligence. The improved fuzzy C-mean clustering method comprises the steps of: firstly, based on the conventional fuzzy C-mean clustering algorithm, improving the fuzzy accuracy of the conventional clustering algorithm by using a novel distance standard in place of a Euclidean standard; meanwhile classifying singly and quickly through using an AFCM (Adaptive Fuzzy C-means) algorithm to replace a randomly distributed initial clustering center to reduce the sensitivity of the clustering algorithm on the initial clustering center; and finally, introducing a QPSO (AQPSO (Adaptive-Quantum Particle Swarm Optimization)) parallel optimization concept based on distance improvement in a clustering process, so that the clustering algorithm has relatively strong overall search capability, relatively high convergence precision, and can guarantee the convergence speed and obviously improve the clustering effect.

Description

technical field [0001] The invention relates to a clustering method, in particular to an improved fuzzy C-means clustering method based on quantum particle swarm optimization, which belongs to the technical field of data mining and artificial intelligence. Background technique [0002] The traditional clustering method belongs to the hard division method, that is, the research object is "either or the other", but in reality, the classification boundaries in science, technology and economic management are not very clear. In order to make the clustering results more in line with the actual requirements, the fuzzy C-means (FCM) algorithm proposed by Dunn "Dunn JC.A fuzzy relative of the ISODATA process and its use in detecting compact well separated cluster [J].JCybemet,1974, v3:32~57" is a classic clustering algorithm that combines the hard C-means clustering algorithm and fuzzy mathematics theory. Although it belongs to a certain class with a certain degree of membership, it ...

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Problems solved by technology

The quality of a clustering analysis process depends on the choice of metrics, many classic algorithms "K.S.Chuang et al, Fuzzy c-means clustering with spatial information for image segmentation, Comput.Med.Imaging Graph.(30)(2006)9 –15.”, “C.H.Cheng, L.Y.Wei, Data spread-based entropy clustering method using adaptive learning, Exp.Syst.Appl.(36)(2009) 12357–12361.” all choose the Euclidean standard, based on this Metric clustering algorithms can generally only find graph or globular clusters of similar size and density, and the resulting clusters are not very precise

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