KMeans

1. What’s KMeans?

KMeans, or K-Means, is a clustering algorithm used in unsupervised learning of Machine Learning to divide a data set into groups or clusters. The objective of the algorithm is to classify the data into “k” groups, where “k” is a predefined number of clusters that the user chooses before running the algorithm, in this way the algorithm will seek to divide the groups taking into account the distance they have. the data with each other.

2. What’s the KMeans function?

The KMeans algorithm plays a vital role in data organization and analysis. Its usefulness lies in the ability to divide data into meaningful clusters, where each cluster shares internal similarities and differs from the others, this is done through a calculation of minimizing the sum of distances between each data and the centroid of its group. This facilitates the identification of patterns, customer segmentation, and trend analysis in various applications. For example, in the case of its application in marketing, customers with similar purchasing behaviors can be grouped, allowing personalized strategies, or these customers can be divided according to their relationship between estimated income and spending on a certain product or service, helping to group customers according to their behaviors when purchasing said product or service. In data analysis, it helps to summarize and understand the underlying structure of the information. However, it is crucial to properly choose the number of clusters (k).

We also have another example of using KMeans in which the Voronoi algorithm is used to have a vision of what the limits of the different clusters would be. In the example this algorithm was used as part of the geospatial analysis. However, this way of using KMeans has other types of applications, such as in the fields of medicine, security, marketing, among others. We address all of this in our blogpost article: “Geospatial analysis: Kmeans in Python with the Voronoi algorithm and OpenStreetMap”.

3. Examples of KMeans.

• Advertising optimization.
• Geospatial analysis.
• User data segmentation.
• Comparison of data groups.
• Quantification and segmentation of images.
• Study of group behavior.
• Inferential analysis using groups as reference.