One fact about machine learning and data algorithms that may surprise business users is that there aren’t that many of them.

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 ...

Another example of clustering algorithms in use is recommender systems, which group together users with similar viewing, browsing, or shopping patterns to recommend similar content.

The k-value at that point is often a good choice. This is called the "elbow" technique. An alternative for clustering mixed categorical and numeric data is to use an old technique called k-prototypes ...

K-Means is a compute-intensive algorithm. The following is the CPU usage of the K-Means algorithm running on large, huge, and gigantic data sizes of HiBench: Fig. 2: CPU usage for large, huge, 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 ...