id: 06106532
dt: j
an: 06106532
au: Chehreghani, Morteza Haghir; Chehreghani, Mostafa Haghir; Abolhassani,
    Hassan
ti: Probabilistic heuristics for hierarchical web data clustering.
so: Comput. Intell. 28, No. 2, 209-233 (2012).
py: 2012
pu: Wiley-Blackwell, Oxford
la: EN
cc: 
ut: data mining; web clustering; Bayesian networks; hierarchical clustering;
    representative point
ci: 
li: doi:10.1111/j.1467-8640.2012.00414.x
ab: Summary: 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.
rv: