Dimensionality Reduction for Association Rule Mining

Provided by: AICIT
Topic: Big Data
Format: PDF
Selecting relevant features from a dataset has been considered to be one of the major components of data mining techniques. Data mining techniques become computationally expensive when used with irrelevant features. Dimensionality reduction/feature selection algorithms are used basically to reduce the dimension(s) of a dataset without reducing the information content of the domain. There are basically two categories of feature selection methods: supervised, where each instance is associated with a class label, and un-supervised, where instances are not related to any class label.

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