Exploring Unsupervised
Machine Learning:
Applications and the Role
of K-Means Clustering
Name : Souvik Pal
Class Roll : 60
University Roll : 10900121064
Paper Name : Machine Learning
Paper Code : PEC-CS701E
What is Unsupervised
Learning?
Unsupervised learning refers to a
type of machine learning where the
model learns from unlabeled data. It
identifies patterns and structures
without explicit instructions, making it
valuable for exploratory data analysis
and discovering hidden relationships.
Key Applications of
Unsupervised Learning
Unsupervised learning has diverse
applications, including customer
segmentation, anomaly detection,
and recommendation systems. These
applications help businesses
understand customer behavior and
improve decision-making processes
by revealing underlying data
structures.
Introduction to K-Means Clustering
K-Means clustering is a widely used algorithm that
partitions data into K distinct clusters based on
feature similarity. It minimizes the variance within
each cluster, making it effective for grouping similar
data points.
How K-Means
Works
The K-Means algorithm involves
selecting K initial centroids,
assigning data points to the closest
centroid, and updating centroids
based on the assigned points. This
process iterates until convergence,
resulting in well-defined clusters.
Advantages of K-Means Clustering
K-Means clustering offers
several advantages: it is simple
to implement, computationally
efficient, and works well with
large datasets. Its ability to
quickly identify patterns makes
it a preferred choice in many
applications.
Challenges in K-
Means Clustering
Despite its strengths, K-Means
clustering faces challenges such as
sensitivity to initial centroid
placement and the requirement to
specify the number of clusters in
advance. These limitations can affect
the quality of clustering results.
Conclusion and In conclusion, unsupervised
learning and K-Means clustering
Future play crucial roles in data analysis.
Directions As data grows, exploring
advanced techniques and hybrid
models will enhance our
capabilities in uncovering insights
and driving innovation.
Thanks!
