Density-based Projected Clustering over High Dimensional Data Streams
By Eirini Ntoutsi, Arthur Zimek, Themis Palpanas, Peer Kröger, Hans-Peter Kriegel. 2012
117 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Tyler L. Hayes
- Christopher Kanan
- Nathan D. Cahill
- Steven C. H. Hoi
- Peilin Zhao
- Doyen Sahoo
- Vincenzo Lomonaco
- Marwan Hassani
In Statistical and Nonlinear Physics
- Peilin Zhao
- Marwan Hassani
- Mohamed Medhat Gaber
- Nathan D. Cahill
- Thomas Seidl
- Steven C. H. Hoi
- Jing Lu
- Pascal Spaus
Citations in the corpus
- Memory Efficient Experience Replay for Streaming Learning (2018, background): “Since many data streams are high-dimensional, there is also work focused on projected subspace clustering [4], [15], [34], [61].”
- Density-Based Projected Clustering of Data Streams (2012, methodology): “HDDStream [18] is a recent density-based projected stream clustering algorithm that was developed simultaneously with PreDeConStream, and published after the first submission of this paper.”
- Online Learning: A Comprehensive Survey (2018, methodology): “The micro-clustering algorithms aim to summarize a data in an online manner, and the clustering is performed using these summaries (Cao et al., 2006; Tasoulis et al., 2007; Ruiz et al., 2009; Li-xiong et al., 2009; Ren and Ma, 2009; Ntoutsi et al., 2012).”