Abstract
Precision anything is the domain of data science, since it relies on identifying patterns or similarities within data. Precision medicine, for example, matches patients to custom-fit medical interventions, based on the patient’s realized affliction or risk profile. Precision marketing matches individuals to information that will change behavior, like voting for a specific candidate or buying a particular brand of shoes. Presenting an advertisement for bathing suits does not make much sense for a consumer in Antarctica. Similarly, different people along the US political spectrum subscribe to different positions on gun ownership, reproductive rights, among other social issues. There is value in targeting; and more efficient targeting is better.
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Notes
- 1.
We illustrate the silhouette method in the following DIY section.
- 2.
This later was revised to only Arlington, VA due to local politics in New York.
- 3.
For simplicity, we define online tech industries using NAICS codes 5182, 5112, 5179, 5415, 5417, and 454111 although we recognize this may exclude sub-industries that are rapidly growing in importance in tech.
- 4.
Hierarchical clustering is technically comprised divisive and agglomerative clustering. The former is a top-down approach, splitting a sample into smaller clusters until each observation is a singleton—reminiscent of decision tree learning. Agglomerative clustering is a bottom-up approach, grouping together observations. Both algorithms are greedy, meaning they make the locally optimal splitting or grouping decision in each iteration.
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The BLS does not consider QCEWÂ to be a time series, but it contains useful information if treated as a time series.
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For ease of analysis, the authors have pre-processed the data. First, the data aggregate monthly records into average quarterly records. Secondly, the data were also seasonally adjusted (SA), meaning that normal year-to-year cycles have been extracted from the data leaving only trend and noise.
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Chen, J.C., Rubin, E.A., Cornwall, G.J. (2021). Cluster Analysis. In: Data Science for Public Policy. Springer Series in the Data Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-71352-2_11
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DOI: https://doi.org/10.1007/978-3-030-71352-2_11
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