Descriptive document clustering via discriminant learning in a co-embedded space of multilevel similarities

Tingting Mu, John Y. Goulermas, Ioannis Korkontzelos, Sophia Ananiadou

Research output: Contribution to journalArticle (journal)peer-review

15 Citations (Scopus)
185 Downloads (Pure)


Descriptive document clustering aims at discovering clusters of semantically interrelated documents together with meaningful labels to summarize the content of each document cluster. In this work, we propose a novel descriptive clustering framework, referred to as CEDL. It relies on the formulation and generation of 2 types of heterogeneous objects, which correspond to documents and candidate phrases, using multilevel similarity information. CEDL is composed of 5 main processing stages. First, it simultaneously maps the documents and candidate phrases into a common co-embedded space that preserves higher-order, neighbor-based proximities between the combined sets of documents and phrases. Then, it discovers an approximate cluster structure of documents in the common space. The third stage extracts promising topic phrases by constructing a discriminant model where documents along with their cluster memberships are used as training instances. Subsequently, the final cluster labels are selected from the topic phrases using a ranking scheme using multiple scores based on the extracted co-embedding information and the discriminant output. The final stage polishes the initial clusters to reduce noise and accommodate the multitopic nature of documents. The effectiveness and competitiveness of CEDL is demonstrated qualitatively and quantitatively with experiments using document databases from different application fields.
Original languageEnglish
Pages (from-to)106-133
Number of pages28
JournalJournal of the Association for Information Science and Technology
Issue number1
Early online date3 Dec 2014
Publication statusPublished - 1 Jan 2016


  • machine learning
  • unsupervised clustering
  • natural language processing
  • text mining
  • information retrieval


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