Finding groups in data: an introduction to cluster analysis

By S. Modak. 2023

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing (2021, background): “While k-means clustered the agents by unit type, it could be argued that more clusters could have been formed, which goes beyond the scope of our work and is considered a feature (and open problem) of unsupervised clustering (Kaufman & Rousseeuw, 2009).”
Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, methodology): “Kaufman and Rousseeuw (2009) use the diameter of continuous clusters as the node level in their program DIANA.”
AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems (2022, methodology): “The popular techniques for solving unsupervised learning tasks are clustering algorithms such as K-means [55], K-Mediods [64], CLARA [45], DBSCAN [27], hierarchical clustering, single linkage [92] or complete linkage [93], BOTS [86], association learning algorithms such as AIS [2], Apriori [3], Apri”