kmeans聚类算法简单示例

Sure, I'd love to help you with that! K-means is a popular clustering algorithm that groups data points into K clusters based on their similarity. Here is a simple example:

Let's say we have a dataset of customer spending habits at a grocery store. We want to group these customers into different clusters based on their spending habits. We can use the k-means algorithm to do this.

First, we need to choose the number of clusters, K. Let's say we want to group customers into 3 clusters. Then, we randomly select 3 points from our dataset to act as the initial centroids of each cluster.

Next, we assign each customer to the nearest centroid based on the distance between their spending habits and the centroid.

After all customers are assigned to a centroid, we recalculate the centroids based on the mean (average) of the spending habits of all customers assigned to that centroid.

We repeat the assignment and recalculation steps until the centroids no longer change or we reach a maximum number of iterations.

Once the algorithm converges, we have grouped all customers into 3 clusters based on their spending habits.

I hope that helps! Let me know if you have any more questions.