The distinction between data reduction and data redundancy
In the previous chapter, Chapter 12, Data Fusion and Data Integration, we discussed and saw an example of the data redundancy challenge. While data redundancy and data reduction have very similar names and their terms use words that have connected meanings, the concepts are very different. Data redundancy is about having the same information presented under more than one attribute. As we saw, this can happen when we integrate data sources. However, data reduction is about reducing the size of data due to one of the following three reasons:
- High-Dimensional Visualizations: When we have to pack more than three to five dimensions into one visual, we will reach the human limitation of comprehension.
- Computational Cost: Datasets that are too large may require too much computation. This might be the case for algorithmic approaches.
- Curse of Dimensionality: Some of the statistical approaches become incapable of finding...
