1. Grouping Similar Data Points
K-Means aims to organize data by placing points with similar characteristics into the same cluster. This makes it easier to understand and analyze large datasets by revealing hidden patterns and relationships.

2. Minimizing Within-Cluster Distance
The algorithm works to ensure that data points within a cluster are as close to one another as possible. This is typically measured using a distance metric such as Euclidean distance, resulting in clusters that are tight and cohesive.

3. Maximizing Between-Cluster Separation
In addition to minimizing distances within clusters, K-Means also seeks to maximize the distance between different clusters. Well-separated clusters make the grouping clearer and help distinguish one cluster from another.