Chapter 17
Compensating for Missing Data
Abstract
In this part of the book Data Science for Software Engineering: Sharing Data and Models, we show that sharing all data is less useful that sharing just the relevant data. There are several useful methods for finding those relevant data regions including simple nearest neighbor, or kNN, algorithms; clustering (to optimize subsequent kNN); and pruning away “bad” regions. Also, we show that with clustering, it is possible to repair missing data in project records.
| Name: | POP1 |
| Also known as: | Reverse nearest neighbor, instance selection. |
| Intent: | When some columns of data are unavailable, use other columns to “stand in” for the missing values. |
| Motivation: | In the early stages of a software project, we cannot ... |
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