Text Mining Research Based on Intelligent Computing in Information Retrieval System
Abstract: With the popularity
and rapid development of the Internet, web text information has rapidly grown
as well. To address the key problem of text mining, text clustering is
investigated in this study. The shuffled frog leaping algorithm as a new type
of swarm intelligence optimization algorithm can be used to improve the
performance of the K-means algorithm, but the shuffled frog leaping algorithm
is influenced by its moving step length. On the basis of this information, the
shuffled frog leaping algorithm is improved, and the K-means clustering
algorithm based on the improved shuffled frog leaping algorithm is introduced.
Experiment results show that the proposed scheme can enhance the ability of
searching for the optimal initial clustering center and can effectively avoid
instability in the clustering results of the K-means clustering algorithm. The
proposed scheme also reduces the chances of the algorithm falling into the
local optimum. The performance of the proposed clustering scheme is found to be
better than that of the clustering algorithm based on the shuffled frog leaping
algorithm.
Keywords: Interest Class
senior high school, DSS, multi attribute decision making (MADM), weighted
product (WP)
Author: Yong Li
Journal Code: jptkomputergg150120
