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How do you choose optimal K?
The Elbow Method This is probably the most well-known method for determining the optimal number of clusters. It is also a bit naive in its approach. Calculate the Within-Cluster-Sum of Squared Errors (WSS) for different values of k, and choose the k for which WSS becomes first starts to diminish.
How can we determine the ideal K in K-means?
There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster.
What type of distance measure is used in K prototype algorithm for measuring the distance between clusters?
Euclidean distance
It measures distance between numerical features using Euclidean distance (like K-means) but also measure the distance between categorical features using the number of matching categories.
How is the K prototype used in clustering?
We are using the Elbow method to determine the optimal number of clusters for K-Prototype clusters. Instead of calculating the within the sum of squares errors (WSSE) with Euclidian distance, K-Prototype provides the cost function that combines the calculation for numerical and categorical variables.
How to determine the optimal k for k-means?
As expected, the plot looks like an arm with a clear elbow at k = 3. Unfortunately, we do not always have such clearly clustered data. This means that the elbow may not be clear and sharp. For Dataset A, the elbow is clear at k = 3. However, this choice is ambiguous for Dataset B.
How does K prototypes work with mixed data types?
K-Prototypes is a lesser known sibling but offers an advantage of workign with mixed data types. It measures distance between numerical features using Euclidean distance (like K-means) but also measure the distance between categorical features using the number of matching categories.
How to find the optimal number of clusters using K?
I am trying to cluster some big data by using the k-prototypes algorithm. I am unable to use K-Means algorithm as I have both categorical and numeric data. Via k prototype clustering method I have been able to create clusters if I define what k value I want. How do I find the appropriate number of clusters for this.?