Frequent Patterns

How many time patterns have appear in the dataset.

Abstract?

It help us to understand the relationships among data. By identifying frequent patterns we can observe strongly correlated items togethers and easily identify similar characteristics and association among them.

Market Basket Analysis:

Market Basket Analysis is one the key techniques used by large retailers to uncover associations between items. Example:

(Bread also tend to buy Butter)

(Laptops also tend to buy bag)

Association Rule Mining: A (IF) -> B (THEN)

There are two type of matrices which help to measure the association algorithm

1. Support Is the frequency of the items brought, Filter out the items which are not frequent bought. Support = freq(A,B) / N

2. Confidence How offend the items A and B occur together, like if person bought A and B but not C then we can rule out

Confidence = freq(A,B) / freq(A)

How does it work?

Association Rule

computer -> antivirus_software [support = 2%, confidence = 60%]
A support of 2% means that 2% of all the transactions under analysis show that computer and antivirus software are purchased together

A confidence of 60% means that 60% of the customers who purchased a computer also bought the software. Confidence tells about the number of times these relationships have been found to be true.

Association rules are considered interesting if they satisfy both a minimum support threshold and minimum confidence threshold.

In general, association rule mining can be viewed as a two-step process:

1. Find all frequent itemsets By definition, each of these itemsets will occur atleast as frequently as a predetermined minimum support count, min_support

2. Generate strong association rules from the frequent itemsets: By definition, these rules must satisfy minimum support and minimum confidence.