Scalability for clustering algorithms revisited

By Fredrik Farnstrom, James Lewis, Charles Peter Elkan. 2000

271 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Memory Efficient Experience Replay for Streaming Learning (2018, methodology): “Partitioning algorithms separate points into j disjoint clusters by minimizing an objective function [2], [20], [28], [43].”
Local Search Methods for k-Means with Outliers (2017, methodology): “[15] used compressionbased techniques to obtain a single-pass algorithm for kmeans.”
A Framework for Clustering Evolving Data Streams (2003, methodology): “TheKDD-CUP’98 Charitable Donation data set has also been used in evaluating several one-scan clustering algorithms, such as [7].”
A Framework for Projected Clustering of High Dimensional Data Streams (2004, methodology): “While the problem of clustering has recently been studied in the data stream environment [3, 8, 11], these methods are for the case of full dimensional clustering.”
Scalable K-Means++ (2012, methodology): “The scalability issues in k -means are addressed by Farnstrom et al. [16], who used compression-based techniques of Bradley et al. [9] to obtain a single-pass algorithm.”
SimPoint 3.0: Faster and More Flexible Program Phase Analysis (2005, background): “Sampling is common in clustering for datasets which are too large to ﬁt in main memory [25, 26].”
Online Machine Learning in Big Data Streams (2018, methodology): “The majority of the most relevant 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].”
Using the Triangle Inequality to Accelerate k-Means (2003, background): “Many papers have been published on on approximating means quickly; well-known papers include (Zhang et al., 1996; Farnstrom et al., 2000).”
Data stream clustering: A survey (2013, methodology): “Not surprisingly, k-means and its variants are widely used in data stream scenarios [Bradley et al. 1998; Farnstrom et al. 2000; O Callaghan et al. 2002; Aggarwal et al. 2003; Zhou et al. 2008; Ackermann et al. 2012].”