Facility location and clustering algorithms constitute a critical area of research that bridges optimisation theory and data analysis. Facility location techniques focus on the strategic placement ...

Economic whiplash like this can be paralyzing. The Federal Reserve’s rate hikes have curbed post-pandemic inflation, but they ...

Entropy Minimization is a new clustering algorithm that works with both categorical and numeric data, and scales well to extremely large data sets.

K-Means Clustering An unsupervised learning algorithm, k-means clustering takes datasets with certain features and values related to these features and groups data points into a number of clusters.

Then, you can use clustering results to custom tailor your marketing efforts. In this course, we will explore two popular clustering techniques: Agglomerative hierarchical clustering and K-means ...

Researchers have developed a new tool, bimodularity, that adds directionality to community detection in networks.

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 evidence shows that flexible, edge-rich spaces, characterized by modular furniture and intuitive spatial cues, increase ...

Statistica Sinica, Vol. 12, No. 1, A Special Issue on Bioinformatics (January 2002), pp. 241-262 (22 pages) Many clustering algorithms have been used to analyze microarray gene expression data. Given ...