When the global model and local model of latent semantic indexing are used to represent medical web pages, the inter-class inclusion of fuzzy clustering results is very large. This paper proposes a new latent semantic difference model, which extracts the text from medical web pages and represents them using the global model, local model and difference model respectively, and uses the FCM algorithm to cluster and calculate the inter-class inclusion. The experiment found that when clustering the given 5 categories of medical web pages, the inter-class inclusion using the difference model is on average about 85% of the global model and 80% of the local model. Keywords: latent semantic indexing; difference model; text mining; FCM clustering; inclusion
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