Using feature construction for dimensionality reduction in big data scenarios to allow real time classification of sequence data

Abstract

A sequence of transactions represents a complex and multi-dimensional type of data. Feature construction can be used to reduce the dataś dimensionality to find behavioural patterns within such sequences. The patterns can be expressed using the blue prints of the constructed relevant features. These blue prints can then be used for real time classification on other sequences.