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 ...

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

For example, K-Means clustering algorithm in machine learning is a compute-intensive algorithm, while Word Count is more memory intensive. For this report, we explore tuning parameters to run K-Means ...

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 ...

Clustering algorithms are a powerful form of AI that can be applied to business challenges from customer segmentation to fraud detection.

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 ...

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 ...