Based on data from its call center, a warranty company thought its market was predominantly female. However, when that ...

Clustering algorithms can be boiled down across many facets of the entire product range to create a smaller, more manageable set of components that form a data map.

[2] The seeding algorithms for spherical k-means clustering. Journal of Global Optimization (2019). [3] A 1.488 approximation algorithm for the uncapacitated facility location problem.

There are many algorithms available for clustering categorical data. However, the algorithm presented here is relatively simple, has worked well in practice, can be applied to both numeric and ...

Density-based Spatial Clustering With Noise (DBSCAN): DBSCAN is an algorithm that groups data points together based on an established distance between them.

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 ...

Clustering non-numeric -- or categorial -- data is surprisingly difficult, but it's explained here by resident data scientist Dr. James McCaffrey of Microsoft Research, who provides all the code you ...

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 ...