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Measuring Resemblance in Sequence Data: An Optimal Matching Analysis of Musicians' Careers
Abstract
This article introduces a method that measures resemblance between sequences using a simple metric based on the insertions, deletions, and substitutions required to transform one sequence into another. The method, called optimal matching, is widely used in natural science. The article reviews the literature on sequence analysis, then discusses the optimal matching algorithm in some detail. Applying this technique to a data set detailing careers of musicians active in Germany in the 18th century demonstrates the practical steps involved in the application of the technique and develops a set of typical careers that successfully categorize most of the actual careers studied by the authors.
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Cites
Rapid similarity searches of nucleic acid and protein data banks.
Wilbur, W. J., & Lipman, D. J. (1983) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.80.3.726 · Cited by 1,546
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Emirbayer, M., & Mische, A. (1998) · American Journal of Sociology · doi:10.1086/231294 · Cited by 6,165
Stress, Coping, and Social Support Processes: Where Are We? What Next?
Thoits, P. A. (1995) · Journal of Health and Social Behavior · doi:10.2307/2626957 · Cited by 3,496
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Pentland, B. T. (1999) · The Academy of Management Review · doi:10.2307/259350 · Cited by 1,307
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(2003) · doi:10.1093/0199251789.001.0001 · Cited by 1,078
Metadata
- Authors
- Abbott, A., & Hrycak, A.
- Venue
- American Journal of Sociology
- Publisher
- University of Chicago Press
- Published
- 1990-07
- Volume
- 96
- Issue
- 1
- Pages
- 144-185
- ISSN
- 0002-9602 (ISSN-L)
- 1537-5390
- OpenAlex
- W2041181884
- Cited by
- 715
- References
- 1
- FWCI
- 25.29
- Type
- journal article
- Language
- en
- Open access
- closed
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- Abbott, A., & Hrycak, A. (1990). Measuring Resemblance in Sequence Data: An Optimal Matching Analysis of Musicians' Careers. American Journal of Sociology. https://doi.org/10.1086/229495
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
