Clustering

Definition of Clustering

Clustering: Clustering is a technique used in data science to group similar items together. This can be useful for organizing data and understanding relationships between different groups of data.

What is Clustering used for?

Clustering is a machine learning technique used to group data points into distinct categories or clusters. This type of unsupervised learning algorithm is used in a variety of applications including customer segmentation, document analysis, object recognition, and anomaly detection.

In its most basic form, clustering involves taking a set of data points and dividing them up into groups that share similar characteristics. Each group is known as a cluster and the data contained within the group can be used to identify patterns or trends within that particular cluster. Clustering algorithms seek out these patterns and help find associations between different variables. For example, if there are two sets of data points representing customers from different cities, clustering can be used to determine what features of each city are associated with the customers in each cluster.

Clustering is also useful in exploratory data analysis as it allows us to get an initial understanding of the structure and relationships between different parts of a dataset. Through grouping together similar data points, we can identify outliers or anomalies which can help provide insights into why certain values may be higher or lower than expected.

Furthermore, clustering algorithms are often used to reduce the dimensionality of datasets by summarizing large amounts of data into smaller clusters. By doing this, we can gain insight into how different clusters relate to one another without having to analyze every single element in the dataset separately. It also makes it easier for us to visualize higher-dimensional datasets where traditional visualization techniques like scatter plots become inefficient due to the sheer number of dimensions being plotted at once.