@article{radulescu2020densitybased,
  title = {Density-based Text Clustering using Document Embeddings},
  author = {Iulia-Maria RADULESCU and Ciprian-Octavian TRUICA and Elena Simona APOSTOL and Alexandru BOICEA and Mariana MOCANU and Daniel Calin POPEANGA and Florin RADULESCU},
  year = 2020,
  url = {https://ibimapublishing.com/p-articles/36AI/2020/36108020/},
  journal = {Communications of International Proceedings},
  volume = 2020 (7),
  abstract = {Density-based clustering algorithms can accurately identify arbitrary shaped clusters, characteristic which makes them advantageous for many real-life datasets. However, most density-based clustering algorithms are affected by the curse of dimensionality, since they rely on distance metrics and range queries. In this paper, we demonstrate how density-based clustering algorithms can exactly cluster short text documents using a modern document embedding model, specifically Doc2Vec. We evaluate the accuracy of a classic density-based clustering algorithm, DBSCAN, and one of its recent variants, HDBSCAN, using two distinct quality functions, the Adjusted Rand Index and the Adjusted Mutual Information.},
  keywords = {Text Clustering, Density Clustering, Document Embeddings},
  note = Article ID: 36108020
}