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Encyclopedia of Database Systems pp 1231–1236Cite as

Document Clustering

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Synonyms

High-dimensional clustering; Text clustering; Unsupervised learning on document datasets

Definition

At a high-level the problem of document clustering is defined as follows. Given a set S of n documents, we would like to partition them into a pre-determined number of k subsets S1, S2, …, Sk, such that the documents assigned to each subset are more similar to each other than the documents assigned to different subsets. Document clustering is an essential part of text mining and has many applications in information retrieval and knowledge management. Document clustering faces two big challenges: the dimensionality of the feature space tends to be high (i.e., a document collection often consists of thousands or tens of thousands unique words); the size of a document collection tends to be large.

Historical Background

Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms as well as in facilitating...

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Recommended Reading

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Author information

Authors and Affiliations

  1. Tsinghua University, Beijing, China

    Ying Zhao

  2. University of Minnesota, Minneapolis, MN, USA

    George Karypis

Authors
  1. Ying Zhao
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  2. George Karypis
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Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. Department of Computer Science and Engineering, The University of California at Riverside, Bourns College of Engineering, Riverside, CA, USA

    Dimitrios Gunopulos

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Zhao, Y., Karypis, G. (2018). Document Clustering. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1479

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