Competition and cooperation clustering method based on maximum clearance segmentation of dynamic bounding box

Abstract

The invention discloses a competition and cooperation clustering method based on maximum clearance segmentation of a dynamic bounding box, and provides a method for acquiring an initial seed point by adopting the maximum clearance segmentation of the dynamic bounding box, i.e. firstly calculating a bounding box of data in a multi-dimensional characteristic space, projecting data points in the bounding box towards a longest axis, dividing the bounding box into two parts by finding out positions with a maximum distance of two adjacent projection points, carrying out the recursion until the entire space is segmented into sufficient subspaces, and finally calculating a center of the subspaces to be used as the initial seed point. The invention also provides a method for merging clusters by adopting a distance radius analysis method and capable of self-adaptively combining a plurality of segmented clusters into a complete cluster aiming at the phenomenon that the same cluster is segmented into a plurality of clusters. By adopting the competition and cooperation clustering method, the missing phenomenon caused by the random seed point can be avoided, the clustering segmentation phenomenon can be avoided, and a real cluster result can be rapidly acquired.

Description

technical field [0001] The invention relates to a competitive and cooperative clustering method based on dynamic bounding box maximum gap segmentation, which belongs to the technical field of data mining. Background technique [0002] Clustering is the process of grouping a group of real or abstract data objects into multiple classes or clusters, and it is an effective means for people to understand and explore the internal relationship between things. Commonly used clustering methods include K-means, ISODATA, and fuzzy clustering. K-means is a clustering method based on the mean square error (MSE) minimization criterion, but this type of algorithm has two main defects: 1) K-means needs to determine the exact number of categories in advance, but in practical applications, It is difficult to determine this parameter; 2) It is easy to produce the so-called "dead unit" phenomenon. If an initial clustering center is given inappropriately, no input data will be assigned to the ...

AI Technical Summary

Problems solved by technology

K-means is a clustering method based on the mean square error (MSE) minimization criterion, but this type of algorithm has two main defects: 1) K-means needs to determine the exact number of categories in advance, but in practical applications, It is difficult to determine this parameter; 2) prone to the so-called "dead unit" phenomenon

However, there are still some unavoidable problems in the CCL algorithm: 1) It has the initial seed point sensitivity problem

[0003] The existence of the above problems affects the use effect and practical value of the CCL clustering algorithm, and it is necessary to improve these defects of the CCL algorithm

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