Multi-core clustering method for rapidly processing missing heterogeneous data

The invention discloses a multi-core clustering method for rapidly processing the missing heterogeneous data. The multi-core clustering method comprises the following steps of 1, carrying out 0 filling initialization on the missing multi-source heterogeneous data; 2, carrying out multi-core learning on the initialized multi-source heterogeneous data by utilizing a plurality of base kernel functions to generate a multi-core matrix; 3, performing multi-core clustering on the generated multi-core matrix to generate a pseudo label; then carrying out the missing value updating on each basic core matrix forming the multi-core matrix by using low-rank estimation; and 4, based on a clustering result, learning the multi-core joint coefficient by using an extreme learning machine. According to the method, the multi-core clustering method is used for achieving the fast learning of the heterogeneous data, the core completion technology is used for fully completing the information of missing data,the clustering performance is improved, and the problem that a traditional multi-core clustering method cannot effectively process the multi-source heterogeneous data is solved.