Many data types arising from data mining applications can be modeled as
bipartite graphs, examples include terms and documents in a text corpus,
customers and purchasing items in market basket analysis and reviewers and
movies in a movie recommender system. In this paper, we propose a new data
clustering method based on partitioning the underlying bipartite graph. The
partition is constructed by minimizing a normalized sum of edge weights between
unmatched pairs of vertices of the bipartite graph. We show that an approximate
solution to the minimization problem can be obtained by computing a partial
singular value decomposition (SVD) of the associated edge weight matrix of the
bipartite graph. We point out the connection of our clustering algorithm to
correspondence analysis used in multivariate analysis. We also briefly discuss
the issue of assigning data objects to multiple clusters. In the experimental
results, we apply our clustering algorithm to the problem of document
clustering to illustrate its effectiveness and efficiency.

本文提出了一种基于分割二分图的新型数据聚类方法，旨在最小化未匹配的顶点之间的边权重总和，通过边权重矩阵的奇异值分解来近似解决这个最小化问题并且在文档聚类问题上取得了显著的效果。