K-means is widely used in various tasks like behavioral segmentation, inventory categorization, and anomaly detection. Once K-means clustering has been performed and the clusters are defined, new data can be easily assigned to these existing groups by calculating the distance of the new data point to each cluster's centroid. The new data point is then assigned to the closest cluster, i.e., the cluster whose centroid has the minimum distance to the data point. This process involves: 1. Calculating the distance between the new data point and each of the cluster centroids obtained from the K-means algorithm. 2. [[Identifying the cluster centroid that is closest to the new data point.]] 3. Assigning the new data point to the cluster associated with the nearest centroid. This process is efficient because it doesn't require recalculating K-means clustering for the entire dataset, including the new data. Instead, it leverages the existing cluster centroids to incorporate new data points into the predefined groups. Determining whether the number of clusters (`n_clusters`) in a clustering algorithm should be dynamic or fixed is context-dependent and can significantly impact the outcome of your clustering task. Static `n_clusters` is suitable when you have prior knowledge of your data's structure or when the number of clusters does not change over time. However, there are scenarios where a dynamic adjustment of `n_clusters` could be advantageous: 1. **Evolving Data**: If your dataset is growing or changing over time, a dynamic approach allows the clustering to adapt to new patterns or groupings in the data. 2. **Unknown Clusters**: When the natural grouping of the dataset is unknown or it's unclear how many clusters best represent the data, dynamically adjusting `n_clusters` can help you explore the data more effectively. https://scikit-learn.org/stable/modules/clustering.html#k-means