Document clustering method based on distribution-convergence model
A document clustering and clustering technology, which is applied in the cross-technology application field of data mining and knowledge system to achieve the effect of clarifying the knowledge context, improving computing efficiency and reducing time overhead.
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[0020] The implementation of the present invention will be described in detail below. The implementation is exemplary and only used to explain the present invention, but not to limit the present invention.
[0021] The document clustering method based on the distribution-convergence model of the present invention comprises the following steps:
[0022] Step 1. Construction method of co-occurrence matrix based on distribution-convergence model
[0023] The present invention first proposes a co-occurrence matrix construction method based on the distribution-convergence model: the distribution-convergence model is used to count the co-occurrence frequency of knowledge attributes in pairs, and combined with the hash graph to construct the co-occurrence matrix, which solves the problem of the limited memory of a single computing node. Problems such as the inability to cluster or the reduction in clustering efficiency caused by storing and processing large matrices.
[0024] (1) Co...
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