Journal of Guangxi Normal University(Natural Science Edition) ›› 2011, Vol. 29 ›› Issue (2): 151-155.

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Algorithm to Cluster Search Results Based on Frequent Itemsets

SHA Bei-bei, XIE Li-cong   

  1. College of Mathematics and Computer Science,Fuzhou University,Fuzhou Fujian 350002,China
  • Received:2011-05-18 Published:2018-11-19

Abstract: Clustering method of search engines can help the users locate the relevant information quickly and efficiently.A method is proposed TS-FIC algorithm which takes the frequent itemsets mining from association rules as class label,and then organizes the initial cluster into the tree structure using thesemantic relations among frequent item sets.When the final cluster is formed,thesemantic similarity is introduced as an approach to compute the class similarity.Finally,by means of a novel ordering scheme,the ordered results can be displayed to the users.The simulation results demonstrate that the proposed algorithmis of certain feasibility and has excellent performance in terms of efficiency and accuracy.

Key words: clustering, association rule, semantic similarity, class labels

CLC Number: 

  • TP391
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