Data clustering is the process of placing data items into groups so that items within a group are similar and items in different groups are dissimilar. The most common technique for clustering numeric ...

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 ...

Because of this, k-means clustering can yield different results on different runs of the algorithm — which isn’t ideal in mission-critical domains like finance.

In this course, we will explore two popular clustering techniques: Agglomerative hierarchical clustering and K-means clustering algorithm. Also, we discuss how to choose the number of clusters and how ...

Disclaimer Columnist: Joseph Opoku Mensah How unsupervised machine learning and K-means clustering are shaping consumer insights « Prev Next » Comments (1) Listen to Article Share: ...

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 ...