1. Distance metrics in pattern recognition
... , the effect of an outlier is dampened as the component differences are not squared. The Manhattan metric is widely used in a variety of data mining algorithms and is justified for those problem domains where it is desired that two data instances should be the same distance when they differ by, say, 4 units along 2 dimensions, as when they differ by 1 unit along ... features may be inadequate to distinguish the different classes 2. The features may be highly correlated 3. The decision boundary may have to be curved 4. There may be distinct subclasses in the data 5. The feature space may ...
- Word Count: 3729
- Approx Pages: 15
- Has Bibliography
- Grade Level: High School