The major weakness of k-means clustering is that it only works well with numeric data because a distance metric must be computed. There are a few advanced clustering techniques that can deal with ...

A key step in deploying clustering is deciding which algorithm to use. One of the most common is k-means, which works by computing the “distances” (i.e., similarity) between data points and ...

In the proposed algorithm, they extend the K-Means clustering process to calculate a weight for each dimension in each cluster and use the weight values to identify the subsets of important ...

K-medoids: This is similar to the k-means, but the center is calculated using a median algorithm. Fuzzy : Each point can be a member of multiple clusters that are calculated using any type of ...