Density-based clustering for real-time stream data

By Yixin Chen, Li Tu. 2007

615 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

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Citations in the corpus

Memory Efficient Experience Replay for Streaming Learning (2018, background): “Density-based algorithms cluster data that lie in highdensity regions of feature space together, while labeling points in low-density regions as outliers [8], [10], [15], [29], [47].”
dbscan: Fast Density-Based Clustering with R (2019, methodology): “These clustering algorithms are widely used in practice with applications ranging from finding outliers in datasets for fraud prevention (Breunig, Kriegel, Ng, and Sander 2000), to finding patterns in streaming data (Chen and Tu 2007; Cao, Ester, Qian, and Zhou 2006), noisy signals (Kriegel and Pfei”
Density‐based clustering (2019, methodology): “Grid-based approaches apply a grid on the data space and aggregate density information on each grid cell (Chen & Tu, 2007).”
Progress in Outlier Detection Techniques: A Survey (2019, methodology): “Grid-based clustering methods: STING [94], Wavecluster [155], DCluster [156] VOLUME 7, 2019 107983 v. Clustering methods for High-Dimensional Data: CLIQUE [157], HPStream [158] In addition to the following algorithms, which have been covered in existing literature [5], [22], [26], [33], a two-phase ”
A Comprehensive Survey of Clustering Algorithms (2015, neutral): “For more detailed information about this kind of clustering algorithms, you can refer to [128–131], D-Stream [41,132].”
Online Learning: A Comprehensive Survey (2018, methodology): “…space into grids, and each instance upon arrival is assigned a grid, and then the clustering is then done based on the density of the grids (Gao et al., 2005; Chen and Tu, 2007; Jia et al., 2008; Tu and Chen, 2009; Wan et al., 2009; Ren et al., 2011; Amini and Ying, 2012; Bhatnagar et al., 2014).”
Density-Based Projected Clustering of Data Streams (2012, methodology): “Detecting clusters of arbitrary shapes in streaming data has been proposed using kernels [13], fractal dimensions [17] and density based clustering [7, 8].”
Density-based Projected Clustering over High Dimensional Data Streams (2012, methodology): “Although there exist methods for density-based clustering over full dimensional data streams, e.g., [7, 8], and density-based clustering over high dimensional static data, e.g., [6,18], there is no algorithm that simultaneously tackles both the high dimensionality and the stream aspects of the data.”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, methodology): “[70] use density-based clustering to cluster data stream in real time, which is important in many”
The ClusTree: indexing micro-clusters for anytime stream mining (2011, methodology): “Detecting clusters of arbitrary shape in streaming data has been proposed using kernels [18], graphs [22], fractal dimensions [6] and density-based clustering [8, 9 ].”
A survey on data stream clustering and classification (2015, methodology): “Minimum distance and mean distance are more popular for data streams, for example, D-Stream [24], Den-Stream [22], and MR-Stream [97] use minimum distance for cluster merging, while STREAM [75], ClusStream [3] usemean distance for cluster merging.”
Clustering Data Streams Based on Shared Density between Micro-Clusters (2016, methodology): “To address this problem, Tu and Chen [9] introduced an extension to the grid-based D-Stream algo-rithm [7] based on the concept of attraction between adjacent grids cells and showed its effectiveness.”
Online Machine Learning in Big Data Streams (2018, methodology): “nt methods are data stream versions of k-means or its variants such as k-medians [237, 35, 80, 177, 104, 9, 239, 96, 143, 3]. Another set of results describes the data stream implementation of DBSCAN [41, 55, 143]. Finally, an online hierarchical clustering algorithm that maintains similarity measur”
Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R (2017, methodology): “DSC_DStream implements the D-Stream algorithm by Chen and Tu (2007).”
A Survey on Ensemble Learning for Data Stream Classification (2017, methodology): “To avoid shortcomings associated with radius based clustering algorithms, in [Sethi et al. 2014] a grid based density clustering method [Chen and Tu 2007] was used on the Structured En-semble for Partially Labeled Streams (SE-PLS) algorithm.”