Open AccessThe Study of an Improved Text Clustering Algorithm for Self-Organizing Maps
Author(s) -
Baolong Zhang,
Zemin Hou
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/428/1/012024
Subject(s) - cluster analysis , cure data clustering algorithm , canopy clustering algorithm , correlation clustering , computer science , single linkage clustering , data stream clustering , selection (genetic algorithm) , artificial intelligence , data mining , determining the number of clusters in a data set , pattern recognition (psychology) , algorithm
The traditional SOM algorithm need to determine the number of clustering categories in advance, which is very subjective. In this paper, an improved k-means initial value selection algorithm is proposed to calculate the number of clustering categories, which is applied to SOM network model. In this algorithm, the Latent Semantic Indexing is applied in the pre-processing stage of clustering, and the improved SOM algorithm is applied in the text clustering stage. Namely, the number of clustering categories obtained by the improved k-means initial value selection algorithm is taken as the number of neurons in the output layer of SOM network.