Online Shopping

Online Shopping

Abstract:
Existing research in association mining has focused mainly on how to expedite the search for frequently co-occurring groups of items in “shopping cart” type of transactions; less attention has been paid to methods that exploit these “frequent itemsets” for prediction purposes. This paper contributes to the latter task by proposing a technique that uses partial information about the contents of a shopping cart for the prediction of what else the customer is likely to buy. Using the recently proposed data structure of itemset trees (IT-trees), we obtain, in a computationally efficient manner, all rules whose antecedents contain at least one item from the incomplete shopping cart. Then, we combine these rules by another technique called Bayesian decision theory to predict the mutually independent items. Finally we introduce a new algorithm based on the Dempster-Shafer (DS) theory of evidence combination which is combined with above techniques to perform well in prediction process.

Existing System:

The existing system focused only on frequently occurring items for prediction process. It uses itemsets trees data structures and rules combined with Bayesian decision theory to predict the mutually independent items. But this approach does not perform well.

Proposed System:

Our proposed system introduces Dempster-Shafer (DS) theory of evidence combination algorithm. DS theory still grow very fast with the average length of the transactions and with the number of distinct items in real world
applications.

Links between tables:

Incoming Itemset
Userid(primary key)
Invoicenum(primary key)
Username
Contactnum
Emailid
Item name
Brandname
Quantity
Date
Item Set
Itemcode
itemname
brandname
unitprice
quantity

Missing Itemset
Invoicenum
Username
Number of missing items
Itemname
Brandname

Voucher
Invoicenum
Username
Number of items
Number of missing items
Total price

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