The k-means algorithm is applicable only for purely numeric data. Data clustering is used as part of several machine-learning algorithms, and data clustering can also be used to perform ad hoc data ...

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 ...

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 ...

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 ...

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.

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

In this paper, the authors contain a partitional based algorithm for clustering high-dimensional objects in subspaces for iris gene dataset. In high dimensional data, clusters of objects often ...