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 ...

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 ...

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 ...

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

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 ...