Using Hidden Markov Model to Perform Sequence Mining in Market Basket Data  
Author Vijay Kumar Ravi

 
Co-Author(s) Jeremy J Blum

 
Abstract Mining market basket data to obtain association rules have been very useful in understanding the customer behaviour. The insights provided through these methods have been useful in making decisions to improve the sales of the product and thus increasing the profit for the product. Studies have shown that another approach in understanding the customer behaviour is to extract the underlying sequence in which the products have been bought by the customer. This is similar to association rule mining but the key is to know the sequence in which it is mined. If the purchase sequence is known from the past history, it would be fairly easy to determine the purchase propensity of the next product.

 
Keywords Sequence Mining, Market Basket Analysis, Hidden Markov Model, Customer Behaviour
   
    Article #:  DSIS19-51
 
Proceedings of ISSAT International Conference on Data Science & Intelligent Systems
August 1-3, 2019 - Las Vegas, NV, U.S.A.