The most common technique for clustering numeric data is called the k-means algorithm. Take a look at the data and graph in Figure 1. Each data tuple has two dimensions: a person's height (in inches) ...

This is the data sparsity problem faced in clustering high-dimensional data. In the proposed algorithm, they extend the K-Means clustering process to calculate a weight for each dimension in each ...

Learn more What are some examples of clustering algorithms? How are clustering algorithms used in specific applications? How are major companies approaching AI clustering? How are challengers and ...

The results look reasonable but many other clusterings are possible. The k-means clustering algorithm minimizes a metric called the within-cluster sum of squares, which will be explained shortly.