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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.
List of Modules:




Item tree generation.
Rule generation mechanism.
Bayesian approach.
DS combination algorithm.
ITEM TREE GENERATION:
This module describes the generation of item sets. These item sets
shows the available stock details. All the items are seperated based on their
category. Here price,discount,quantity of each items can be maintained by
administrator. Each item can be identified by separate item code.
Eg:
Items
Health drinks
Boost
Complan
Sweets
Chocolates
Milk sweets
.
.
RULE GENERATION MECHANISM:
The proposed rule generation algorithm makes use of the flagged
item tree created from the training data set. The algorithm takes an incoming
itemset as the input and returns a graph that defines the association rules
entailed by the given incoming itemset.
To expedite the rule generation process, we use the Item tree
approach that modifies the rule generation algorithm due to two reasons. First,
the algorithm addresses a slightly different task, generating all rules of the form.
Second, our goal is not to generate all association rules, but, rather, to build a
predictor from a set of “effective” association rules.
BAYESIAN APPROACH:
The mathematically “clean” version is known to be computationally
expensive in domains where many independent variables are present.
Fortunately, this difficulty can be sidestepped by the so called Naive-Bayes
principle that assumes that all variables are mutually pairwise conditionally
independent.The process of identifying mutually independent items known as
Bayesian decision theory.
DS COMBINATION ALGORITHM:
When searching for a way to predict the presence or absence of an item
in a partially observed shopping cart, we wanted to use association rules. The
question is how to combine the potentially conflicting evidence. One possibility is
to rely on the Dempster-Shafer-based Association Rule Mining(DS-ARM) theory
of evidence combination.
DS theory assigns to any set, a numeric value called a basic belief
assignment (BBA) or mass that quantifies the evidence one has towards the
proposition that the given attribute values. This process also provides Body of
Evidence(BoE). This BoE describes what are all the evidences to predict the
missing items.
OVERVIEW OF PROPOSED SYSTEM:
Database Design:
1)Itemset Table:
Field Name
Data Types
Itemcode(primary int
key)
itemname
Varchar
Description
Separate code for each code.
brandname
Varchar
Brand name of item
unitprice
Float
Unit price of each item
quantity
Int
Quantity of each item
Name of the item
2)Incoming Itemset table:
Field Name
Data Types
Userid(primary key) Int
Description
Separate id for each user
Invoicenum(primary
key)
Username
Contactnum
Emailid
Item name
Brandname
Int
Quantity
Date
Int
Datetime
Separate number for each order
list
Name of the user
Contact number of the user
Email id of user
Name of th item ordered by user
Brandname of the item ordered
by user
Quantity of each item
Ordered date
Varchar
Int
Varchar
Varchar
Varchar
3)Missing Items table:
Field Name
Invoicenum
Data Types
Int
Username
Number of missing
items
Itemname
Brandname
Varchar
Int
Description
Separate number for each order
list
Name of the user
Number of missing items
Varchar
Varchar
Name of the missing item
Brandname of the missing item
4) Voucher table:
Field Name
Invoicenum
Data Types
Int
Username
Number of items
Number of missing
items
Total price
Varchar
Int
Varchar
Description
Separate number for each order
list
Name of the user
Number of incoming items
Number of the missing item
float
Grand total of items
Links between tables:
Incoming Itemset
Userid(primary key)
Invoicenum(primary
key)
Item Set
Itemcode
itemname
Username
brandname
Contactnum
unitprice
Emailid
quantity
Item name
Brandname
Quantity
DateVoucher
Invoicenum
Missing Itemset
Invoicenum
Username
Username
Number
items
Number of items
Itemname
Number of missing
items
Brandname
Total price
of
missing