In K-Means clustering, the number of clusters, k, must be specified in advance.

Yes, in K-Means clustering, the number of clusters (k) must indeed be specified in advance. This is one of the limitations of this method. The algorithm doesn't know the number of clusters (k) in the data set in advance, so it must be provided by the user. Here are the steps involved in K-Means clustering:

1. Initialization: Choose k initial cluster centers. These can be randomly selected from the data points.

2. Assignment: Assign each data point to the nearest cluster center. The distance is usually calculated using Euclidean distance.

3. Update: Recalculate the cluster centers as the mean (centroid) of all data points assigned to each cluster.

4. Repeat steps 2 and 3 until the cluster assignments no longer change or a maximum number of iterations is reached.

The choice of k is critical in determining the quality of the clustering. A common method for choosing k is the elbow method, which involves plotting the explained variation as a function of the number of clusters, and picking the elbow of the curve as the number of clusters to use.