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 ...

The k-means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, are common in many applications. Mainstream approaches to ...