SUBMITED BY
K.MOHAN KUMAR
V.SUDHAKAR
M.RAMU KUMAR
N.P.AKHIL

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ABSTRACT
Existing research in association with 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 project 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 item set trees (IT-trees), we obtain, in a computationally efficient manner, all rules whose antecedents contain at least one item from the incomplete shopping cart.
This project combine these rules by uncertainty processing techniques, including the classical Bayesian decision theory and a new algorithm based on the Dempster-Shafer (DS) theory of evidence combination.
Existing system:
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.
Existing system Disadvantages:
They didn’t find missing items in frequently used item set.
couldn't find number of users per item set.
Time complexity
lack of viewing items to the user.
Proposed system:
*Finding missing items using apriority algorithms in frequently used item set.
*counting number of users per item set.
*calculating total number of visitor’s in our websites
Advantages of Proposed system:
Reducing time complexity. User can easily view the items set. *
Missing items can easily find in the item set.
Hardware Requirements
SYSTEM : Pentium IV 2.4 GHz
HARD DISK : 40 GB
FLOPPY DRIVE : 1.44 MB
MONITOR : 15 VGA colour
MOUSE : Logitech.
RAM : 256 MB
Software Requirements
Operating system :- Windows XP Professional
Front End : - VS.NET 2005
Coding Language :- Visual C# .Net
Back-End : - Sql Server 2000