Hierarchical clustering method based on mutual shared nearest neighbors

Abstract

The invention discloses a hierarchical clustering method based on mutual shared nearest neighbors. The hierarchical clustering method comprises the steps of firstly calculating a nearest neighbor matrix T1 and a nearest neighbor matrix T2 of a whole data set D; calculating a nearest ranking matrix M according to the nearest neighbor matrix T1 and the nearest neighbor matrix T2; calculating a local density through the nearest ranking matrix M to obtain a submanifold set; finally calculating the similarity of submanifolds, and gathering the submanifolds to obtain a final partitioning result. According to the hierarchical clustering method based on mutual shared nearest neighbors, the problem of low clustering precision caused by point partitioning errors during generation of the submanifold set in the conventional sparsification and graph partitioning processes based on K near graph clustering is solved.

Description

Technical field [0001] The invention belongs to the technical field of data mining of computer science and technology, and relates to a hierarchical clustering method based on mutual shared nearest neighbors. Background technique [0002] Cluster analysis is an important research topic in the field of data mining. Clustering technology has been widely used in telecommunications, retail, biology, marketing and other fields. Clustering is an unsupervised classification, which is used to find data points that are concentrated in the characteristics of the object itself and clustered into clusters, and to ensure that the clusters have the largest possible similarity and the largest possible dissimilarity between clusters . Existing clustering algorithms are generally divided into: 1. Partition-based clustering algorithms represented by K-means, Fuzzy K-means, and k center points; 2. Level-based clustering algorithms represented by QROCK, CURE, BIRCH, Clustering algorithms; 3. Dens...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a hierarchical clustering method based on mutual shared nearest neighbors, which solves the problem of point division errors in the generation of sub-cluster sets in the process of sparse and graph division based on K-nearest neighbor graph clustering. The problem of low clustering accuracy

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