Image for K-means Clustering

K-means Clustering

K-means clustering is a data analysis method used to group similar items together. Imagine you have a collection of items and want to sort them into categories. K-means starts by picking a set number of groups, or "clusters." It then assigns each item to the nearest cluster based on its features. After that, it recalculates the center of each cluster and reassigns items accordingly. This process repeats until the clusters stabilize, meaning items no longer change groups. Ultimately, K-means helps to uncover patterns and relationships in data, making it easier to understand and analyze complex information.

Additional Insights

  • Image for K-means Clustering

    K-Means Clustering is a method used in data analysis to group similar items together based on shared characteristics. Imagine you have a collection of different colored marbles, and you want to sort them into groups of similar colors. K-Means works by first choosing a set number of groups, then assigning each item to the nearest group based on its features. It repeats this process, adjusting the groups until they are as distinct as possible. This helps in identifying patterns and organizing data in a meaningful way, making it easier to interpret and analyze.