The elbow method is a technique used to determine the optimal number of clusters (K) in K-means clustering.
The elbow method involves plotting the within-cluster sum of squares (WCSS) against different values of K and identifying the "elbow" point in the plot. The elbow point represents the value of K where the rate of decrease in WCSS slows down significantly. It is often considered as an indication of an appropriate number of clusters.
It involves plotting the within-cluster sum of squares (WCSS) against different values of K and identifying the "elbow" point in the resulting plot.
The steps are
Perform K-means clustering with various values of K, starting from a minimum value to a maximum value.
For each value of K, calculate the WCSS, which represents the sum of squared distances between data points and their assigned cluster centers.
Plot the number of clusters (K) on the x-axis and the corresponding WCSS on the y-axis.
Examine the plot and identify the "elbow" or "knee" point where the decrease in WCSS slows down significantly.
The elbow point is often considered as an indication of an appropriate number of clusters. It suggests that adding more clusters beyond that point may not lead to a significant improvement in the clustering quality. The location of the elbow point can vary depending on the dataset and the underlying structure of the data.
It's worth noting that the elbow method provides a heuristic approach to estimate the optimal number of clusters and may not always give a definitive answer. It should be used in combination with other methods and considerations to make an informed decision about the appropriate value of K.