Can I read Imbalanced Clustering With Theoretical Learning Bounds on EtoBox?

Imbalanced Clustering With Theoretical Learning Bounds by Trần Doanh is a document available to read on EtoBox.

What is Imbalanced Clustering With Theoretical Learning Bounds about?

The document discusses imbalanced clustering, a challenging problem in data mining where the number of samples varies across clusters. It introduces a novel k-Means algorithm with Adaptive Cluster Weight (MACW) and an improved model called Imbalanced Clustering with Theoretical Learning Bounds (ICTLB), both aimed at addressing the uniform effect in clustering. The effectiveness of ICTLB is validated through comprehensive experiments on various imbalanced datasets, demonstrating its superiority over existing

Author

Trần Doanh

Language

EN