Market Based Analysis is one of the key techniques used by large relations to show associations between items.
it can generate association rules from the given transactional datasets.Association rules are useful for analyzing and predicting customer behavior.
The disadvantage of association algorithms is require much more time to run than a decision tree algorithm.
The A priori Algorithm is an instrumental algorithm for mining familiar item sets.
The disadvantage is more exploration space and computational cost is too expensive.
Products Frequently Bought Together in Stores Using classification models
1. Products Frequently Bought Together in Stores
Using classification models
Supervised by
Dr. Amina dahim
2023 - 2024
Submitted by M.Sc. Students
Sabreen Salih Mahdi
Zahraa Fouad Rasool
Hiba Tullah Ziyad
2.
Introduction .
Motivation of frequent item sets in online stores .
Association rule .
Association rule Advantages/ Disadvantages .
A-priori Algorithm for computing frequent item sets .
A-priori Algorithm Advantages/ Disadvantages .
What Applications use A-priori Algorithm .
Conclusion .
Outlines
3.
Introduction
Frequently bought together purchase
recommendations are one of the most impactful
stores strategies that have the potential to
maximize the return on investment on in-house
product stores.
Frequently bought together are the products that
your customers usually purchase with specific
items in your store.
Displaying the right products at the right time can
help drive the discovery and sales of your
products.
4.
One of the most important strategies
for influencing sales is the offering of
discounts.
A discount would enable the
salesperson to increase the average
order value of the transactions.
Smart upselling and cross-selling
technique that fits into natural buying
habit.
Introduction
5.
Motivations behind frequent sets of products in
online stores
An easy way to offer shopping assistance.
The frequently bought together section act
as a shopping assistant by suggesting the
best-suited complementary products.
Manually curating frequently bought together
products lets you maximize revenue and
profits for your store
6.
products as frequently bought together
would help you remind your customers
of the products that they might need.
Products that bought together helps
you boost your sales and profits.
Motivations behind frequent sets of products in
online stores
7.
This concept itself is derived from the terminology of market basket
analysis, namely the search for relationships of several products in a
purchase transaction.
Most machine learning algorithms work with numeric datasets and
hence tend to be mathematical. However, association rule mining is
suitable for non-numeric, categorical data.
Understanding consumer buying behavior is compulsory in business
Association rule Mining
8.
Given a set of transactions, each of which is a set of items, find all
rules (XY) that satisfy user specified minimum support and
confidence constraints.
Support = (#T containing X and Y)/(#T)
Confidence = (#T containing X and Y)/ (#T containing X)
Applications
Cross selling and up selling
Supermarket shelf management
Association rule Mining
19.
Advantages The employed algorithms have too many parameters for
someone who is not a data mining expert.
The disadvantage of association algorithms is that they are trying to find
patterns within a potentially very large search space and, hence, can require
much more time to run than a decision tree algorithm.
Association rule Advantages/ Disadvantages
20.
A priori Algorithm : is an significant algorithm for mining frequent
item sets for Boolean association rules.
It contains two processes:-
Detect all frequent itemsets by scanning DB.
Form strong association rules in the frequent itemsets.
A priori pruning principle: If there is any itemset which is infrequent,
its superset should not be generated/tested!
A- priori Algorithm for computing frequent itemsets
21.
Method:
Initially, scan DB once to get frequent 1-
itemset
Generate length (k+1) candidate itemsets
from length k frequent itemsets
Test the candidates against DB
Terminate when no frequent or candidate
set can be generated
A- priori Algorithm for computing frequent itemsets
22.
Transaction
ID
Items
T1 Hot Dogs , Buns , ketchup
T2 Hot Dogs , Buns
T3 Hot Dogs , Coke , Chips
T4 Chips , Coke
T5 Chips , ketchup
T6 Hot Dogs , Coke , Chips
Find the frequent itemsets on this table, assume that minimum support
count = 3
TDB
Itemset Sup- count
Hot Dogs 4
Buns 2
ketchup 2
Coke 3
Chips 4
C1
1ST SCAN
Itemset Sup- count
Hot Dogs 4
Coke 3
Chips 4
Compare candidate
support count with
minimum support count
L1
23.
Itemset
Sup-
count
Hot Dogs , Coke 2
Hot Dogs , Chips 2
Coke , Chips 3
Find the frequent itemsets on this table, assume that minimum support
count = 3
Itemset
Hot Dogs , Coke
Hot Dogs , Chips
Coke , Chips
Itemset Sup- count
Coke , Chips 3
C2
2nd scan
L2
Compare candidate
support count with
minimum support
count
24.
Advantages
- Uses large itemset property
- Easily parallelized
- Easy to implement
Disadvantages
- Assumes transaction database is memory resident
- Requires many database scans
A- priori Algorithm Advantages/ Disadvantages
26.
Market Based Analysis is one of the key techniques used by large relations to show
associations between items.
it can generate association rules from the given transactional datasets.
Association rules are useful for analyzing and predicting customer behavior.
The disadvantage of association algorithms is require much more time to run than a decision
tree algorithm.
The A priori Algorithm is an instrumental algorithm for mining familiar item sets.
The disadvantage is more exploration space and computational cost is too expensive.
Conclusion
