Market Basket Analysis is an interesting concept wherein a set of historical purchase data is studied extensively and data-mining techniques are applied to predict a user’s purchase behavior. In the resent times, every e-commerce giant, like Amazon, Flip-kart etc. are trying
to increase their sales by this technique. It has been found in study that when a user is purchasing an item then he/she might be interested in purchasing other items too. So, by analyzing this purchase behavior if we can recommend other products at the time of purchasing then that could increase
the sale and profit as well. Analyzing the correlation among the products by studying the purchase pattern and then finding the associated products is the main objective of Market Basket Analysis. Several algorithms have been developed to find the association among the products. But most of
the traditional algorithms are based on support value of the product. They do not consider another important factor i.e. the marginal contribution of the product. Considering this into account in this paper we are going to propose a Shapley Value based game theoretic approach for market basket
analysis. Shapley Value is a well-known solution concept in cooperative game theory. It gives the measure of marginal contribution of a player in a cooperative game.
A multi-image hiding technique in dilated video regions based on cooperative game-theoretic approach
