TY - GEN
T1 - Discovering meaningful clusters from mining software engineering literature
AU - Wu, Yan
AU - Siy, Harvey
AU - Fan, Li
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84886892151&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84886892151&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84886892151
SN - 9781627486620
T3 - 20th International Conference on Software Engineering and Knowledge Engineering, SEKE 2008
SP - 613
EP - 618
BT - 20th International Conference on Software Engineering and Knowledge Engineering, SEKE 2008
T2 - 20th International Conference on Software Engineering and Knowledge Engineering, SEKE 2008
Y2 - 1 July 2008 through 3 July 2008
ER -