A Framework for Parallelizing Hierarchical Clustering Methods
| Autor: | Benjamin Moseley, Kefu Lu, Thomas Lavastida, Silvio Lattanzi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Hierarchical clustering ComputingMethodologies_PATTERNRECOGNITION Scalability 0202 electrical engineering electronic engineering information engineering Unsupervised learning 020201 artificial intelligence & image processing Artificial intelligence Cluster analysis business Scale (map) computer 0105 earth and related environmental sciences |
| Zdroj: | Machine Learning and Knowledge Discovery in Databases ISBN: 9783030461492 ECML/PKDD (1) |
| DOI: | 10.1007/978-3-030-46150-8_5 |
| Popis: | Hierarchical clustering is a fundamental tool in data mining, machine learning and statistics. Popular hierarchical clustering algorithms include top-down divisive approaches such as bisecting k-means, k-median, and k-center and bottom-up agglomerative approaches such as single-linkage, average-linkage, and centroid-linkage. Unfortunately, only a few scalable hierarchical clustering algorithms are known, mostly based on the single-linkage algorithm. So, as datasets increase in size every day, there is a pressing need to scale other popular methods. |
| Databáze: | OpenAIRE |
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