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Text Mining Research Based on Intelligent Computing in Information Retrieval System

Journal from gdlhub / 2016-11-16 07:22:01
Oleh : Yong Li, Telkomnika
Dibuat : 2015-12-01, dengan 1 file

Keyword : Interest Class senior high school, DSS, multi attribute decision making (MADM), weighted product (WP)
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/1788

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.

Deskripsi Alternatif :

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.

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