Weblog Clustering in Multilinear Algebra Perspective

TL;DR

This paper introduces a novel weblog clustering approach using multilinear algebra, specifically PARAFAC tensor decomposition, to identify important blogs and shared descriptive words more effectively than traditional methods.

Contribution

The paper presents a new multilinear algebra-based clustering method that simultaneously groups similar blogs and their key descriptive words, emphasizing importance.

Findings

Effective identification of important blogs and shared words

Clusters of blogs and words are ranked by importance

Method outperforms standard co-clustering techniques

Abstract

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.