Discovering meaningful clusters from mining software engineering literature

Yan Wu, Harvey Siy, Li Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Document clustering is becoming an increasingly popular technique for identifying relationships in unstructured text. In this paper, we attempt to make sense of the output of a clustering algorithm applied to software engineering research papers. We introduce a notion of cluster "stability" as a measure of the meaningfulness of a cluster. We assess its usefulness and limitations in identifying meaningful clusters. In the process, we track how important research topics may have changed from year to year.

Original languageEnglish (US)
Title of host publication20th International Conference on Software Engineering and Knowledge Engineering, SEKE 2008
Pages613-618
Number of pages6
StatePublished - 2008
Event20th International Conference on Software Engineering and Knowledge Engineering, SEKE 2008 - San Francisco Bay, CA, United States
Duration: Jul 1 2008Jul 3 2008

Publication series

Name20th International Conference on Software Engineering and Knowledge Engineering, SEKE 2008

Conference

Conference20th International Conference on Software Engineering and Knowledge Engineering, SEKE 2008
CountryUnited States
CitySan Francisco Bay, CA
Period7/1/087/3/08

ASJC Scopus subject areas

  • Software

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