Final answer:
The k-means algorithm is an iterative clustering process consisting of selecting initial centroids, assigning points to the nearest centroid, updating the centroids, and repeating until convergence. Overfitting in k-means can be avoided by selecting an appropriate number of clusters, multiple initializations, and cross-validation.
Step-by-step explanation:
K-Means Algorithm Iterative Learning Process
The k-means algorithm is a popular clustering method that involves partitioning a dataset into k distinct clusters based on feature similarity. The iterative learning process of k-means includes the following steps:
Initial cluster center selection: Choose k initial cluster centroids randomly or by some heuristic.
Assignment step: Assign each data point to the nearest cluster centroid.
Update step: Re-calculate the centroids of the clusters based on the data points assigned to them.
Repeat steps 2 and 3 until the centroids no longer change significantly, indicating convergence.
The process results in a separation of the data into clusters that minimize the variance within each cluster.
Overfitting Problem in K-Means and Solutions
Overfitting in k-means occurs when the model adjusts too closely to a particular dataset, potentially capturing noise as if it were a significant pattern. This can lead to clusters that do not generalize well to new data. To avoid overfitting in k-means:
The choice of k should be justified by domain knowledge, the elbow method, or other statistical techniques.
Running the algorithm with different initializations and choosing the one with the best performance could help mitigate overfitting.
Using cross-validation techniques to ensure the clusters have predictive power on separate data.