Contents
When Should K-means be used?
The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data. This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets.
Is K-means widely used?
Within the universe of clustering techniques, K-means is probably one of the mostly known and frequently used. K-means uses an iterative refinement method to produce its final clustering based on the number of clusters defined by the user (represented by the variable K) and the dataset.
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.
What’s the relationship between k means and 1 nearest neighbor?
The algorithm has a loose relationship to the k -nearest neighbor classifier, a popular machine learning technique for classification that is often confused with k -means due to the name. Applying the 1-nearest neighbor classifier to the cluster centers obtained by k -means classifies new data into the existing clusters.
What are the advantages and disadvantages of k-means?
Clustering data of varying sizes and density. k-means has trouble clustering data where clusters are of varying sizes and density. To cluster such data, you need to generalize k-means as described in the Advantages section.
Why do we need advanced versions of k-means?
As k increases, you need advanced versions of k-means to pick better values of the initial centroids (called k-means seeding ). For a full discussion of k- means seeding see, A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm by M. Emre Celebi, Hassan A. Kingravi, Patricio A. Vela.