Gauss mixture model tree and incremental clustering method thereof
A Gaussian mixture model and clustering method technology, applied in database models, special data processing applications, instruments, etc., can solve problems such as unacceptable computational complexity and time complexity
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[0062] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.
[0063] The incremental clustering method adopted in this embodiment includes data insertion, cluster tree update, data deletion and clustering result determination. The relationship between these four technical links is: for each new data, it needs to be inserted into the existing Gaussian mixture model tree, and then the clustering tree is updated according to the inserted result; with the insertion of new data, check that it has been inserted into the cluster Whether the data of the tree needs to be deleted, and if it needs to be deleted, delete the data; when all the data is read, the clustering result is determined. Process such as figure 1 shown.
[0064] The structure of data clustering is as figure 2 Shown, G 1 to G 5 are five leaf nodes, corresponding to a single Gaussian component, corresponding to the densest data distribution; GMM 1...
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