The k-means clustering algorithm minimizes a metric called the within-cluster sum of squares, which will be explained shortly. [Click on image for larger view.] Figure 2: K-Means Demo Data Before 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 ...

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 ...