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Nonnegative Matrix Factorization (NMF) is useful when there are many attributes and the attributes are ambiguous or have weak predictability. By combining attributes, NMF can produce meaningful patterns, topics, or themes.
NMF is especially well-suited for text mining. In a text document, the same word can occur in different places with different meanings. For example, hike can be applied to the outdoors or to interest rates. By combining attributes, NMF introduces context, which is essential for predictive power:
"hike" + "mountain" -> "outdoor sports""hike" + "interest" -> "interest rates"