Unraveling the Mysteries: A Beginner’s Guide to Clustering in Machine Learning
Machine Learning, a branch of Artificial Intelligence, is often used by businesses to uncover valuable insights from data. One of the popular techniques of Machine Learning is Clustering. Clustering is an unsupervised learning method in which data points are grouped together based on their similarities.
Understanding Clustering
Clustering is a method of dividing data points into homogeneous groups called clusters. Each cluster consists of data points that are similar to each other and different from the data points in other clusters. Clustering is useful in various fields such as marketing, finance, healthcare, and more.
Types of Clustering
There are two types of Clustering: Hierarchical and Partitional.
Hierarchical Clustering
Hierarchical Clustering is divided into two categories- Agglomerative and Divisive.
Agglomerative is a bottom-up approach wherein each data point is considered as a separate cluster and then gradually merged with other clusters based on similarity until a large cluster is formed. The algorithm continues until all data points are in one cluster.
Divisive, on the other hand, follows a top-down approach and starts with a large cluster, which is then divided into smaller clusters based on differences among data points.
Partitional Clustering
Partitional Clustering involves dividing data points into non-overlapping clusters. This method works by assigning each data point to a random cluster initially and then optimizing the assignments to minimize the distance between data points in a cluster.
Applications of Clustering
Clustering finds its use in various fields like Customer Segmentation, Image and Text Segmentation, Anomaly Detection, and more.
Customer Segmentation is used by businesses to group similar customers together based on demographic, psychographic, and behavioral characteristics.
Image and Text Segmentation is useful for object recognition or in Natural Language Processing (NLP) tasks such as Text Classification, Summarization, and more.
Anomaly Detection is used by businesses to detect unusual behavior in a system, which helps them identify and solve issues before they become major problems.
Conclusion
Clustering is widely used in Machine Learning and has a variety of applications across multiple industries. Understanding the types of Clustering, and its applications can help businesses gather valuable insights from their data. With the availability of huge datasets, Clustering can be an efficient and effective method for businesses to understand their data and make informed decisions.
