Welcome to the clustering project. Clustering is the process of dividing a dataset into groups such that the membersof each group are as similar (close) as possible to one another, and different groups are as dissimilar (far) as possible from one another. Clustering can uncover previously undetected relationships in a dataset. This project we will learn to implement what we have learnt.
- Introduction to Clustering
- k-means clustering
- Hierarchical clustering
- Clustering in Python
- We are going to learn how to implement the K-means clustering algorithm and uncover the hidden insights.
- Also we are going to learn how to implement another well known technique of clustering that is Hierarchical Clustering.
- You will learn to classify data into individual clusters by performing K-means Clustering.
- Learn to implement the technique Hierarchical clustering which spots and establish Hierarchical relationship in your data.
- We will be using the inbuilt Dataset of library
sklearn.datasets.load_digits()which will consist of images of digits from 1 to 9.