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@article{145499,
        author = {A.Aruna and S.Sajida},
        title = {Emporium Design Framework For Supermarket Using Data Mining Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {10},
        pages = {518-520},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145499},
        abstract = {Data mining is the prsocess of identifying patterns in large data sets. A dataset is a collection of data and it involves methods at the intersection of machine learning, statistics and database systems. In existing system the routine emporium layout in the supermarkets based on their business logic implementation, which means the products are placing their some of practical attributes or characteristics in the particular area. We will find out the product categories and their improvements, the emporium layout can be responsible for the product categories by the manufacturers or category buyers. Mainly this is company or organization oriented and it fails to respond to the needs of the time pressured customer.  Some retailers are trying to move from this organization to consumer oriented is the disadvantage of this system. To overcome this problem we are introduced this proposed model. 
This paper proposes a new store layout approach based on the association rule mining. Here efficient apriori algorithm is used to improve the customer satisfaction and profit of the supermarket. We assume that attractive store layout, navigational aids, sales people contact and in-store events induce transitions from recreational shopping to purchase oriented shopping, whereas retail crowding and time pressure engender shift from purchase-oriented shopping to recreational shopping. In addition, it is predicted that environmental design characteristics have greater impact on shopping path than on purchase decision marketing interventions exert more influence on purchase decision than on movement while contextual factors have comparable effect on shopping path and purchase decision. Retailers can utilize the proposed model to dynamically improve their in-store conversion rate.
},
        keywords = {Data mining, Apriori algorithm, Emporium, Supermarket.},
        month = {},
        }