Fast ICA for online cashflow analysis

Authors:
Shangming Yang;Zhang Yi
Affiliations:
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

Independent component analysis is an important tool for separating blind sources and also a well-known method of finding latent structure in data. With the observed data and some of their properties, one can find original sources and structures of these data. This can be used in data mining to discover useful information from the online data. In this paper, based on the Fast ICA algorithm, a model for chain stores' sales is set up. Using this model, we can analyze the distributions of the sold products from the online cashflow in each store of the chain; the cashflow is from the mixture of different products in a store. This algorithm will be very attractive for a commercial enterprise to provide information for making their future sales plan.