Two-stage hybrid particle swarm optimization clustering method

Abstract

The invention relates to a two-stage hybrid particle swarm optimization clustering method, which is mainly used for solving the problems of greater time consumption and low accuracy of the conventional particle swarm optimization K-mean clustering method when the number of dimensions of samples is higher. The technical scheme disclosed by the invention comprises the following steps: (1) reading a data set and the number K of clusters; (2) taking statistics on information of dimensionality; (3) standardizing the dimensionality; (4) calculating a similarity matrix; (5) generating a candidate initial clustering center; (6) performing particle swarm K-mean partitional clustering; and (7) outputting a particle swarm optimal fitness value and a corresponding data set class cluster partition result. According to the two-stage hybrid particle swarm optimization clustering method disclosed by the invention, the first-stage clustering is firstly performed by adopting agglomerative hierarchical clustering, a simplified particle encoding way is provided, the second-stage clustering is performed on data by particle swarm optimization K-mean clustering, the advantages of hierarchical agglomeration, K-mean and particle swarm optimization methods are integrated, the clustering speed is accelerated, and the global convergence ability and the accuracy of the clustering result of the method are improved.

Description

technical field [0001] The invention belongs to the field of computer technology, and further relates to a two-stage hybrid particle swarm optimization clustering method in the field of data mining technology. The invention can be widely used in data compression, information retrieval, character recognition, image segmentation and text clustering, etc., and at the same time It can have a wide range of applications in biology, marketing, and abnormal data detection. Background technique [0002] Cluster analysis is an important means and method of data division or grouping processing in data mining. It does not require any prior knowledge, and through a certain similarity measurement criterion, similar samples are classified into a cluster. The purpose of clustering is to make the similarity of samples in the same cluster larger, and the similarity of samples between different clusters smaller. Small. In biology, cluster analysis can be used to cluster the genes of organism...

AI Technical Summary

Problems solved by technology

Use the first-stage clustering hierarchical agglomerative clustering to obtain the initial cluster center sub-cluster set with high accuracy as the search space for the initial cluster center of the particle swarm optimization K-means clustering algorithm for the second-stage clustering, and the unselected The sub-clusters used as the clustering center are all broken up and re-divided into clusters, so as to overcome the disadvantage of not being able to change the class of the object after a certain merger or split of the hierarchical clustering algorithm, and reduce the impact of particle swarm optimization on K-means clustering. The random selection of the initial clustering center is sensitive and easy to fall into local optimal problems, improving the accuracy of clustering

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