Application of K-means Algorithm to Web Text Mining Based on Average Density Optimization
FAN Guang-Ling, LIU Yu-Wei, TONG Jan-Qiang, ZHAO Sheng-Hai, NIE Zhi-Quan
Journal of Digital Information Management, Vol. 14, No. 1, 2016.
DOI: 
Link: http://dline.info/fpaper/jdim/v14i1/v14i1_6.pdf
Abstract:
Text information is increasing at an explosive
speed with the advent of the Internet. However,
this situation has given rise to the problem of abundant
information with relative deficiency of knowledge. Therefore,
finding a way to seek target information rapidly
and accurately has become a research hotspot. This
study presented a method to improve web text clustering
accuracy and integrity. First, the dk-means algorithm
was modified, and the k-means algorithm based
on average density optimization was proposed. Second,
a web text clustering model was designed, and indepth
research on the key technology of web text clustering
was conducted. Finally, the k-means algorithm
based on average density optimization (Adk-means algorithm)
was applied to the web text clustering model,
and clustering and classification of web text were completed.
Experiment showed that the purity and mutual
trust values of the Adk-means algorithm are higher than
those of the dk-means algorithm, and the modified algorithm
is greatly improved in terms of accuracy, integrity,
and performance of partitioned clusters. When clustering
text, the Adk-means algorithm has high polymerization
and similarity within classification. Research
results were applicable to text clustering. When used
in Internet text searching, the Adk-means algorithm is
a highly efficient information retrieval technology that
can improve searching speed and accuracy.