Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination

Authors

Michael Muller, IBM Research
Shion Guha, Marquette UniversityFollow
Eric P.S. Baumer, Cornell UniversityFollow
David Mimno, Cornell University
N. Sadat Shami, IBM

Document Type

Conference Proceeding

Language

eng

Publication Date

2016

Publisher

Association for Computing Machinery (ACM)

Source Publication

Proceedings of the 19th International Conference on Supporting Group Work (GROUP '16)

Source ISSN

9781450342766

Abstract

Grounded Theory Method (GTM) and Machine Learning (ML) are often considered to be quite different. In this note, we explore unexpected convergences between these methods. We propose new research directions that can further clarify the relationships between these methods, and that can use those relationships to strengthen our ability to describe our phenomena and develop stronger hybrid theories.

Comments

Published as part of the proceedings of the conference, Proceedings of the 19th International Conference on Supporting Group Work (GROUP '16): 3-8. DOI.

Recommended Citation

Muller, Michael; Guha, Shion; Baumer, Eric P.S.; Mimno, David; and Shami, N. Sadat, "Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination" (2016). Mathematics, Statistics and Computer Science Faculty Research and Publications. 515.
https://epublications.marquette.edu/mscs_fac/515