Premium
PROBABILISTIC HEURISTICS FOR HIERARCHICAL WEB DATA CLUSTERING
Author(s) -
Haghir Chehreghani Morteza,
Haghir Chehreghani Mostafa,
Abolhassani Hassan
Publication year - 2012
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2012.00414.x
Subject(s) - computer science , cluster analysis , data mining , hierarchical clustering , brown clustering , cure data clustering algorithm , single linkage clustering , correlation clustering , canopy clustering algorithm , artificial intelligence , machine learning
Clustering Web data is one important technique for extracting knowledge from the Web. In this paper, a novel method is presented to facilitate the clustering. The method determines the appropriate number of clusters and provides suitable representatives for each cluster by inference from a Bayesian network. Furthermore, by means of the Bayesian network, the contents of the Web pages are converted into vectors of lower dimensions. The method is also extended for hierarchical clustering, and a useful heuristic is developed to select a good hierarchy. The experimental results show that the clusters produced benefit from high quality.