Kmeans clustering groups similar data points together from "summary" of Data Science For Dummies by Lillian Pierson
Kmeans clustering is a popular method used in data science to group similar data points together. This technique works by partitioning a dataset into K number of clusters based on the similarity of data points. The goal is to minimize the distance between data points within the same cluster while maximizing the distance between different clusters.
The algorithm starts by randomly selecting K initial cluster centers. Each data point is then assigned to the nearest cluster center based on a distance metric, such as Euclidean distance. After all data points have been assigned to clusters, the cluster centers are recalculated as the mean of all data points in the cluster. This process continues iteratively until the cluster centers no longer change significantly.
One of the key advantages of Kmeans clustering is its simplicity and scalability. It is a straightforward algorithm that is easy to implement and can handle large datasets efficiently. However, the e...
Read More
Continue reading the Microbook on the Oter App. You can also listen to the highlights by choosing micro or macro audio option on the app. Download now to keep learning!
Now you can listen to your microbooks on-the-go. Download the Oter App on your mobile device and continue making progress towards your goals, no matter where you are.
