In the previous modules, you saw various supervised machine learning algorithms. Supervised machine learning algorithms make use of labelled data to make predictions.
For example, an email will be classified as spam or ham, or a bank’s customer will be predicted as ‘good’ or ‘bad’. You have a target variable Y which needs to be predicted.
On the other hand, in unsupervised learning, you are not interested in prediction because you do not have a target or outcome variable. The objective is to discover interesting patterns in the data, e.g. are there any subgroups or ‘clusters’ among the bank’s customers?
Let’s learn clustering in detail.
So you saw the favourite tourist destinations of Prof. Dinesh and Rohit. You also saw the emerging pattern in the places preferred by the Professor and Rohit. However, how does all this relate to the concept of unsupervised learning?
PRACTICAL APPLICATIONS OF CLUSTERING
- Customer Insight: Say, a retail chain with so many stores across locations wants to manage stores at best and increase the sales and performance. Cluster analysis can help the retail chain to get desired insights on customer demographics, purchase behaviour and demand patterns across locations. This will help the retail chain for assortment planning, planning promotional activities and store benchmarking for better performance and higher returns.
- Marketing: Cluster Analysis can help with In the field of marketing, Cluster Analysis can help in market segmentation and positioning, and to identify test markets for new product development.
- Social Media: In the areas of social networking and social media, Cluster Analysis is used to identify similar communities within larger groups.
- Medical: Cluster Analysis has also been widely used in the field of biology and medical science like human genetic clustering, sequencing into gene families, building groups of genes, and clustering of organisms at species.
In the next segment, you will be introduced to a real-life application of clustering — grouping customers of an online store into different clusters and making a separate targeted marketing strategy for each group. We will be using this example throughout the module.