Locally Consistent Concept Factorization for Document Clustering
Locally Consistent Concept Factorization for Document Clustering
复制标题
文档聚类的局部一致概念分解
DOI：
10.1109/tkde.2010.165
复制
发表时间：
2011-06-01
影响因子：
8.9
通讯作者：
Han, Jiawei
中科院分区：
文献类型：
--
作者：
Cai, Deng;He, Xiaofei;Han, Jiawei
以往的研究表明，在低维线性子空间中，文档聚类性能可以得到显著提高。最近，基于矩阵分解的技术，如非负矩阵分解（NMF）和概念分解（CF），已经取得了令人印象深刻的结果。然而，这两个有效地看到只有全球欧几里德几何，而当地的流形几何没有充分考虑。在本文中，我们提出了一种新的方法来提取文档的概念，这是一致的流形几何，使每个概念对应于一个连接组件。我们的方法的核心是一个图形模型，它捕获的文件子流形的局部几何形状。因此，我们称之为局部一致概念分解（LCCF）。通过使用图Laplacian平滑文档到概念的映射，LCCF可以提取关于内在流形结构的概念，从而与相同概念相关联的文档可以很好地聚类。在TDT 2和路透社-21578上的实验结果表明，该方法具有更好的表示性，在准确性和互信息方面取得了更好的聚类结果。
Previous studies have demonstrated that document clustering performance can be improved significantly in lower dimensional linear subspaces. Recently, matrix factorization-based techniques, such as Nonnegative Matrix Factorization (NMF) and Concept Factorization (CF), have yielded impressive results. However, both of them effectively see only the global euclidean geometry, whereas the local manifold geometry is not fully considered. In this paper, we propose a new approach to extract the document concepts which are consistent with the manifold geometry such that each concept corresponds to a connected component. Central to our approach is a graph model which captures the local geometry of the document submanifold. Thus, we call it Locally Consistent Concept Factorization (LCCF). By using the graph Laplacian to smooth the document-to-concept mapping, LCCF can extract concepts with respect to the intrinsic manifold structure and thus documents associated with the same concept can be well clustered. The experimental results on TDT2 and Reuters-21578 have shown that the proposed approach provides a better representation and achieves better clustering results in terms of accuracy and mutual information.