This paper describes a clustering method to group the most similar and
important weblogs with their descriptive shared words by using a technique from
multilinear algebra known as PARAFAC tensor decomposition. The proposed method
first creates labeled-link network representation of the weblog datasets, where
the nodes are the blogs and the labels are the shared words. Then, 3-way
adjacency tensor is extracted from the network and the PARAFAC decomposition is
applied to the tensor to get pairs of node lists and label lists with scores
attached to each list as the indication of the degree of importance. The
clustering is done by sorting the lists in decreasing order and taking the
pairs of top ranked blogs and words. Thus, unlike standard co-clustering
methods, this method not only groups the similar blogs with their descriptive
words but also tends to produce clusters of important blogs and descriptive
words.