A segment-based approach to clustering multi-topic documents
A segment-based approach to clustering multi-topic documents
复制标题
DOI：
10.1007/s10115-012-0556-z
复制
发表时间：
2012
影响因子：
2.7
通讯作者：
Andrea Tagarelli;G. Karypis
中科院分区：
文献类型：
--
作者：
Andrea Tagarelli;G. Karypis
文档聚类一直被认为是文本数据管理的核心问题。当文件内容的特点是分主题讨论而不一定彼此相关时，这一问题就变得特别具有挑战性。现有的文档聚类方法传统上假设文档是用于文本表示和相似度计算的不可分割的单元，这可能不适合处理具有多个主题的文档。在本文中，我们通过利用文本段中文档的自然组合来解决多主题文档聚类的问题，这些文档相对于底层的子主题是一致的。我们提出了一种新的文档聚类框架，该框架旨在通过识别原始文档中基于片段的部分的内聚组来归纳文档组织。我们在大量的多主题文档上实证证明了我们的基于片段的方法的重要性，并将其与传统的文档聚类方法进行了比较。
Document clustering has been recognized as a central problem in text data management. Such a problem becomes particularly challenging when document contents are characterized by subtopical discussions that are not necessarily relevant to each other. Existing methods for document clustering have traditionally assumed that a document is an indivisible unit for text representation and similarity computation, which may not be appropriate to handle documents with multiple topics. In this paper, we address the problem of multi-topic document clustering by leveraging the natural composition of documents in text segments that are coherent with respect to the underlying subtopics. We propose a novel document clustering framework that is designed to induce a document organization from the identification of cohesive groups of segment-based portions of the original documents. We empirically give evidence of the significance of our segment-based approach on large collections of multi-topic documents, and we compare it to conventional methods for document clustering.