StreamKM++: A clustering algorithm for data streams

By Marcel R. Ackermann, Marcus Märtens, Christoph Raupach, Kamil Swierkot, Christiane Lammersen, Christian Sohler. 2010

371 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].”
Online Learning: A Comprehensive Survey (2018, neutral): “Another method is the StreamKM++ (Ackermann et al., 2012).”
Fair Coresets and Streaming Algorithms for Fair k-Means Clustering (2018, background): “In addition to the above cited coreset constructions, coresets for k -means in particular have also been studied empirically [1, 3, 25, 27, 33].”
BICO: BIRCH Meets Coresets for k-Means Clustering (2013, methodology): “We compare BICO experimentally with popular and very fast heuristics (BIRCH, MacQueen [24]) and with approximation algorithms (StreamKM++ [2], StreamLS [16, 26]) with the best known quality guarantees.”
Learning in Nonstationary Environments: A Survey (2015, methodology): “In unsupervised learning/clustering, the learning is done without using labeled data [114]–[116], whereas in active learning (not to be confused with active approaches to learning in nonstationary environments, discussed in Section III-A) the algorithm identifies the most important instances for the”
Twitter spammer detection using data stream clustering (2014, methodology): “To add the ability for k-means++ to update its clusters as new points arrive from a data stream, StreamKM++ was developed [1].”
The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race (2017, methodology): “The approach in [31] considers vectors made of 126 features extracted from both accounts and tweets as input of modiﬁed versions of the DenStream [8] and StreamKM++ [1] clustering algorithms, to cluster feature vectors of a set of unlabeled accounts.”
MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering (2010, neutral): “– StreamKM++ [1]: It computes a small weighted sample of the data stream and it uses the k-means++ algorithm as a randomized seeding technique to choose the first values for the clusters.”
K-variates++: More Pluses in the K-means++ (2016, background): “…extending k-means clustering to these settings has been a major research area in recent years, there has been no obvious solution to tailoring kmeans++ (Ackermann et al., 2010; Ailon et al., 2009; Bahmani et al., 2012; Balcan et al., 2013; Liberty et al., 2014; Shindler et al., 2011) (and others).”
Online Machine Learning in Big Data Streams (2018, neutral): “er. Online clustering algorithms are surveyed among other places in [165, 131, 7, 207]. 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]. Another set of results describes the data stream implem”
Scalable K-Means++ (2012, methodology): “[1] introduced another streaming algorithm based on k-means++ and show that it performs well while making a single pass over the input.”