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Model
Clustering Model Training
Unlabelled Data Clustering on the Basis of Type
Clustering – Example 1
Rohan is the owner of daily needs departmental store. He wants to improve his marketing strategies by
grouping customers based on their purchasing behaviour. The dataset contains different details about the
customers like age, purchasing power, and their annual expenditure (Money spent to buy from the store).
Your aim is to group the customers into clusters like:
a. High income high spending patterns
b. Young and low income customers with average spending patterns
c. Middle aged customers with moderate spending patterns.
In this example clustering is used where the algorithm will analyse the data and group customers into clusters
based on similarities in their spending behaviour and income.
The outcome will be clusters under the following categories:
• Cluster 1: High-income customers with high spending.
• Cluster 2: Young customers with low spending.
• Cluster 3: Middle-aged customers with moderate spending.
This is how clustering technique works. The clustering model will be able to identify clusters based on some
similarities or patterns which are not defined in the input. For example, age, purchasing power and the annual
amount spent on the purchases (in store) are the only features known, but clusters based on age and purchasing
power have been grouped together and given as output. Once the clusters are made, the store can offer
personalised discounts or loyalty programs to high-value customers and target low-spending customers with
promotional offers to increase their spending.
Difference between Classification and Clustering
Classification: It's a supervised learning technique that assigns data points to predefined categories or labels.
Labelled data is fed to the model. For example, classifying emails as "Spam" or "Not Spam."
Clustering: It is an unsupervised learning technique used to group similar data points into clusters. The input data is
unlabelled data. For example, grouping customers based on shopping behaviour to target personalised marketing.
Association
Association is an unsupervised learning method that is used to find interesting relationships or patterns among
variables in a dataset. It is widely employed to identify relationships between items, sets frequently purchased
items together in large databases, helping to analyse how items or events are related to each other.
Advanced Concepts of Modeling in AI 127

