The Chinese keywords extraction algorithm based on association rule mining

Classical algorithms of keywords extraction can hardly get low computational complexity and high accuracy. The association rule mining based algorithm is proposed in this paper. This algorithm adopts improved FP-Growth algorithm to extract word co-occurrence information, utilizes the similarity algorithm to eliminate synonyms, and removes noisy words and simplified features of candidates, thus reducing the storage space and the amount of calculation in the condition of high precision and recall rate. The experimental results have shown that the average F value of the corpus reaches 61%, which is higher than classical algorithms, and that support degree is the vital influence factor.