Conference Paper

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

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Text/Conference Paper

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Date

2016

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Source

Proceedings of the 2016 ACM International Conference on Supporting Group Work

Publisher

Association for Computing Machinery

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.

Description

Muller, Michael; Guha, Shion; Baumer, Eric P.S.; Mimno, David; Shami, N. Sadat (2016): Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination. Proceedings of the 2016 ACM International Conference on Supporting Group Work. DOI: 10.1145/2957276.2957280. Association for Computing Machinery. pp. 3–8. Sanibel Island, Florida, USA

Keywords

supervised learning , coding families , machine learning , axial coding , unsupervised learning , grounded theory

Citation

DOI

10.1145/2957276.2957280

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GROUP 2016