Clustering data with the K-Means algorithm
Clustering is one of the most popular unsupervised learning techniques. This technique is used to analyze data and find clusters within that data. In order to find these clusters, we use a similarity measurement such as the Euclidean distance to find subgroups. This similarity measure can estimate the tightness of a cluster. Clustering is the process of organizing data into subgroups whose elements are like each other.
The goal of the algorithm is to identify the intrinsic properties of data points that make them belong to the same subgroup. There is no universal similarity metric that works in all cases. For example, we might be interested in finding the representative data point for each subgroup, or we might be interested in finding the outliers in the data. Depending on the situation, different metrics might be more appropriate than others.
The K-Means algorithm is a well-known algorithm for clustering data...
