Multi-view clustering method based on constrained non-negative matrix factorization

Abstract

Aiming at the clustering problem of a high-dimensional multivariate time sequence, the invention provides a multi-view clustering method based on constrained non-negative matrix factorization, in order to effectively carry out sample clustering, multivariate time sequence data is firstly projected to a multi-relation network, and then a hierarchical method is adopted to generate a plurality of independent views. The method also includes, secondly, clustering the plurality of views by adopting a constrained non-negative matrix factorization method; and finally, solving in an alternate iterativeoptimization mode, and performing k-means clustering on the representation matrix obtained by calculation of the solving result to obtain a clustering result. The correctness and effectiveness of themethod and the algorithm provided by the invention are verified through experiments, and experimental results show that the clustering method has relatively high accuracy and effectiveness.

Description

technical field [0001] The invention relates to the technical field of time series data mining, in particular to a multi-view clustering method based on constrained non-negative matrix decomposition. Background technique [0002] Multivariate time-series data is widely used in daily life. However, for the clustering of multivariate time-series data, existing algorithms often deal with it from a single view, ignoring the hidden multi-view information in multivariate time-series data. Multi-view clustering can mine the consistent and complementary information in different views and improve the clustering effect, so it has become a current research hotspot. Scholars at home and abroad have proposed many algorithms. [0003] Graph-based multi-view clustering uses a unified graph matrix to represent the similarity relationship between data samples, and clusters on the basis of this graph matrix to obtain results. Zhan et al. learn a consistency map by minimizing the inconsistenc...

AI Technical Summary

Problems solved by technology

[0006] The above studies mainly focus on the multi-view clustering problem. However, the existing research does not perform well on multivariate time-series data.

Images