Evaluation Metrics for Unsupervised Learning Algorithms

2019/05/14 by Palacio-Niño, Julio-Omar, Berzal, Fernando · 3 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · doi:10.48550/arxiv.1905.05667

Abstract

Determining the quality of the results obtained by clustering techniques is a key issue in unsupervised machine learning. Many authors have discussed the desirable features of good clustering algorithms. However, Jon Kleinberg established an impossibility theorem for clustering. As a consequence, a wealth of studies have proposed techniques to evaluate the quality of clustering results depending on the characteristics of the clustering problem and the algorithmic technique employed to cluster data.

Cited by

• Mixed Data Clustering Survey and Challenges
• CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction
• Layout-Aware OCR for Black Digital Archives with Unsupervised Evaluation

Related

• Clustering - What Both Theoreticians and Practitioners are Doing Wrong
• A Critical Note on the Evaluation of Clustering Algorithms
• Pointwise Metrics for Clustering Evaluation
• Clustering validity based on the most similarity
• A Family of Metrics for Clustering Algorithms