The document discusses machine learning as a branch of artificial intelligence, focusing on market-basket analysis, a technique used by retailers to find associations between frequently purchased items. It explains key measures in association rule mining, including support, confidence, and lift. The document also provides examples and references for further reading on the topic.

1. Akash A. Borse
M.Tech (ACDS)
PRN: 170847980005
22/03/2018

2. What is Machine Learning ?
A major field from an umbrella term called “Artificial
Intelligence”…
Applying set of rules defined by humans over machines to
make them self working and self learning.

3. What is Market –Basket Analysis ?
A technique used by large retailers to identify associations
among items.
It works by looking for combinations of items that occurs
frequently in transactions.
It uses “Association Rules” for analyzing combinations of
frequent items set.

4. Lets Understand Association Rule…
It’s a technique to know how items are associated to each
other.
Factors to be considered to measure association:
1. Support
2. Confidence
3. Lift

5. 1. Support:
• It states how popular an itemset is. It is measured by
proportion of transactions in which itemset appears.
2. Confidence:
• It states how likely an item Y is purchased when item X is
purchased {X -> Y}. It is measured by the proportion of
transaction with item X, In which item Y also appears
3. Lift:
• It states how likely an item Y is purchased when item X is
purchased, while controlling for how popular an item Y is.

6. Lets have an example…
Source: kdnuggets

7. Source: kdnuggets

8. References:
https://www.kdnuggets.com/2016/04/association-rules-
apriori-algorithm-tutorial.html
https://machinelearningmastery.com/market-basket-
analysis-with-association-rule-learning/
https://www.quora.com/What-are-association-rules-in-
data-mining