Abstract
Similar text algorithm is the main method for news topic extraction and online public opinion hot spot tracking. The traditional text similarity method has the problems of high data dimension and high computational complexity. This paper proposes a method of news text clustering based on hash feature and random mapping. The method based on hash feature reduces the dimensionality of the text. At the same time, the article clustering preprocessing based on random mapping can speed up retrieval. Experiments show that the algorithm proposed in this paper performs well on related data sets.
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