K-means clustering

Definition of K-means clustering

K-means clustering: K-means clustering is a data mining algorithm used to partition a set of data points into k clusters. Data is divided into clusters based on the similarities of the points within each cluster. This algorithm is often used to segment customers into different groups for marketing purposes.

How is K-means clustering used?

K-means clustering is a type of unsupervised learning algorithm used for data analysis and exploration. It’s a type of clustering technique, which means it divides data into groups (clusters) based on similarity or distance between the data points. The goal of K-means clustering is to separate data points into clusters such that each cluster has its own unique characteristics or properties. This type of clustering can be used in applications such as market segmentation, image compression, and anomaly detection.

K-means clustering works by first assigning a number of clusters to the data set and then running an iterative process to move data points to the closest cluster centroid, which is the mean position of all the data points in the cluster. The algorithm continues this process until no more improvements can be made by reassigning any point from one cluster to another. The result is a partitioning of the given dataset into k distinct non-overlapping subsets that are as close together as possible within their respective clusters.

K-means clustering has many advantages over other types of clustering algorithms, including its simplicity and efficiency. It does not require any prior knowledge about the structure of your data; all it needs is some basic assumptions about how similar two data points are to one another. Additionally, K-means clustering is easy to implement and fast to run compared with other clustering algorithms, making it very useful in situations where quick results are needed or time constraints are an issue. Finally, K-means can easily adapt itself when new data points enter or leave the dataset since it only needs to recalculate the centroids instead of reanalyzing all the existing clusters like some other algorithms would require.

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