in k-means algorirthm
时间: 2024-05-21 22:15:20 浏览: 8
In K-means algorithm, the goal is to partition a given dataset into K clusters (where K is a predetermined number) based on their similarity. The algorithm works by:
1. Initialization: Randomly selecting K data points from the dataset as initial cluster centers.
2. Assigning data points to clusters: For each data point in the dataset, calculate its distance to each of the K cluster centers and assign it to the cluster whose center is closest.
3. Updating cluster centers: Recalculate the center of each cluster by taking the mean of all the data points assigned to it.
4. Repeating steps 2 and 3 until convergence: Repeat steps 2 and 3 until the cluster assignments no longer change or a maximum number of iterations is reached.
The algorithm aims to minimize the sum of squared distances between each data point and its assigned cluster center, also known as the within-cluster sum of squares (WCSS). The optimal number of clusters can be determined by plotting the WCSS against the number of clusters and selecting the elbow point, where the rate of decrease in WCSS slows down significantly.
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