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An Approach of Vectorizing Shopping Paths Sensed by RFID Tags to Classify Retail Customers and Its Application with Principal Component Regression
Author(s) -
Inamoto Tsutomu,
Ohno Asako,
Murao Hajime
Publication year - 2014
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.11588
Subject(s) - vectorization (mathematics) , tuple , principal component analysis , computer science , sequence (biology) , relation (database) , principal (computer security) , component (thermodynamics) , path (computing) , regression analysis , pattern recognition (psychology) , data mining , artificial intelligence , mathematics , machine learning , physics , thermodynamics , discrete mathematics , parallel computing , biology , genetics , programming language , operating system
SUMMARY In this paper, we propose an approach for classifying customers in retail stores into given types according to their shopping paths, each of which is a sequence of sections visited by the corresponding customer and is gathered by an RFID tag. The approach vectorizes a sequence of sections; that is, the approach splits such a sequence into tuples of sections, then sums up the occurrence counts of those tuples. This vectorization is based on the hypothesis that a customer's type has a relation to subsequences of sections in his/her shopping path and the conjecture that customers types can be attributed to co‐occurrences of such subsequences. After vectorization, the proposed approach applies a general discrimination method to such vectors of equal length. In computational illustrations, principal component regression is selected as a representative of general discrimination methods and is applied to shopping paths collected in an existing retail store so as to predict whether a customer purchases items more than average or not. Computational results demonstrate the effectiveness of the proposed approach, with higher forecast accuracies than existing methods.