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Latent Dirichlet Allocation, which is a non-supervised learning method, can be used for topic detection, automatic text categorization, keyword extraction and so on. It only focuses on the text itself, not considering other external correlation properties. External association property refers to some structured
always ignores relativity of the topic. These affect the topic discovery and topic trend. Therefore, combining with the keywords combination and Word2Vec model to strength expression of semantic information in topic clustering, this article sets weighted K-means algorithm for topic discovery. The results show our weighted K
aggregation function for textual data. Our approach is based on the affinity between keywords and uses the search of cycles in a graph to find the aggregated keywords. We also present performances and a comparison with three other methods. The experimental study shows good results for our approach.
Excel as the unit of encryption and decryption and analyzes the file structure. Then, the model automatically identifies the types of data, verifies the legitimacy of data and filters the keywords which will not be encrypted and decrypted. At last, we invoke special algorithms to encrypt or decrypt the data. The
model in the data set of flickr. The final ranked pictures are the combination of keywords and users' preference matching. The experiment proves that our method is better than both non-personalization method and common personalization method.
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