Introduction to the Market Basket Analysis and Association Rule Mining. Strength, weakness of MBA in Data Mining.

1. By Narayan Vyas
Submitted To – Dr. Vishal Dutt
Dept. of Computer
Science, MDSU, Ajmer

2. What is Market Basket Analysis?
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Market Basket Analysis is one of the key techniques used by large
retailers to uncover associations between items. It works by
looking for combinations of items that occur together frequently in
transactions.
To put it another way, it allows retailers to identify relationships
between the items that people buy.

3. Introduction
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Association Rule Mining is used when you want to find an association
between different objects in a set, find frequent patterns in a transaction
database, relational databases or any other information repository. The
applications of Association Rule Mining are found in Marketing, Basket Data
Analysis (or Market Basket Analysis) in retailing, clustering and classification.
The most common approach to find these patterns is Market Basket Analysis,
which is a key technique used by large retailers like Amazon, Flipkart etc. to
analyze customer buying habits by finding associations between the different
items that customers place in their “shopping baskets”.

4. Using Association Rule Mining
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• Changing the store layout according to trends
• Customer behavior analysis
• Catalog design
• Cross marketing on online stores
• What are the trending items customers buy
• Customized emails with add-on sales etc..

5. Amazon’s Webpage
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6. Introduction
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To carry out an MBA you’ll first need a data set of transactions. Each transaction represents
a group of items or products that have been bought together and often referred to as an
“itemset”. For example, one itemset might be: {pencil, paper, staples, rubber} in which case
all of these items have been bought in a single transaction.
In an MBA, the transactions are analyzed to identify rules of association. For example, one
rule could be: {pencil, paper} => {rubber}. This means that if a customer has a transaction that
contains a pencil and paper, then they are likely to be interested in also buying a rubber.
Before acting on a rule, a retailer needs to know whether there is sufficient evidence to
suggest that it will result in a beneficial outcome.

7. Introduction
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Support - the percentage of transactions that contain all of the items in an itemset (e.g.,
pencil, paper and rubber). The higher the support the more frequently the itemset occurs.
Rules with a high support are preferred since they are likely to be applicable to a large
number of future transactions.
Confidence - the probability that a transaction that contains the items on the left hand side of
the rule (in our example, pencil and paper) also contains the item on the right hand side (a
rubber). The higher the confidence, the greater the likelihood that the item on the right hand
side will be purchased or, in other words, the greater the return rate you can expect for a
given rule.

8. 8
Problems
• Level of Frequency of appearance
determination
• Finding strong association among
frequent items.
Functions of ARM
• Finding set of items that have
significant impact on business.
• Collecting info. From numerous
transactions.
• Generating rules from counts in
transactions.
Strength of ARM
• Easy to implement.
• Easy to start.
• Flexible data formats.
• Simplicity.
Weakness
• Exponential Growth in computation.
• Rule selection.
• Not suitable for rare items (suitable
for Frequent Itemsets).

9. 9
Thank You