Clustering Data Streams: Theory and Practice

By Sudipto Guha, Adam Meyerson, Nina Mishra, Rajeev Motwani, Liadan O'Callaghan. 2003

943 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].”
A survey on data stream clustering and classification (2015, background): “As we may only compute and store a small summary of the data streams and possibly throw away the rest of the data; approximate results are acceptable [17,39,45].”
Scalable K-Means++ (2012, methodology): “Their al-gorithm builds on an inﬂuential paper of Guha et al. [17] who gave a streaming algorithm for the k -median problem that is easily adaptable to the k -means setting.”
k-means++: the advantages of careful seeding (2007, background): “Even though there is an “obvious” clustering, the uniform seeding will inevitably merge some of these clusters, and the local search will never be able to split them apart (see [12] for further discussion of this phenomenon).”
Data Mining for Internet of Things: A Survey (2014, background): “Among these surveys, a comprehensive overview of the IoT from three different angles, things, internet, and semantics, was presented by Atzori and his colleagues [3].”
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].”
Streaming Pattern Discovery in Multiple Time-Series (2005, positive): “[23] propose a one-pass k-median clustering algorithm.”
Clustering Data Streams Based on Shared Density between Micro-Clusters (2016, background): “Over the last 10 years many algorithms for clustering data streams have been proposed [5], [6], [8], [9], [14], [15], [16], [17], [18], [19], [20].”
Ranking a stream of news (2005, methodology): “Weremark that this model describes a framework where one can plug-in diﬀerent data stream clustering algorithms (see [1, 9] and the references therein) for creating and weighting the set of edges E 2 .”
Fuzzy c-Means Algorithms for Very Large Data (2012, background): “Representative algorithms include incremental clustering [23], [24] and divide-and-conquer approaches [25], [26].”
Clustering algorithms: A comparative approach (2016, methodology): “This requires the application of novel approaches to quickly process the entire volume of continuously incoming data [93], the detection of new clusters that are formed and the identification of outliers [94].”
Density-Based Clustering over an Evolving Data Stream with Noise (2006, methodology): “Previous methods, one-pass [4, 10, 11] or evolving [1, 2, 5, 18], do not consider that the clusters in data streams could be of arbitrary shape.”
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].”
A data placement strategy in scientific cloud workflows (2010, neutral): “Todaytheyare widelyusedtoprocessdatastreams[38].”
Mining data streams: a review (2005, methodology): “[27, 28] have studied analytically clustering data streams using K-median technique.”
Data clustering: 50 years beyond K-means (2008, neutral): “Because of the high-speed processing requirements, many of the data stream clustering methods (Guha et al., 2003a; Aggarwal et al., 2003; Cao et al., 2006; Hore et al., 2009b) are extensions of simple algorithms such as K-means, Kmedoid, fuzzy c-means, or density-based clustering, modified to work…”
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.”
Density-based clustering for real-time stream data (2007, methodology): “Earlier clustering algorithms for data stream uses a single-phase model which treats data stream clustering as a continuous version of static data clustering [9].”
An Algorithm for Online K-Means Clustering (2014, background): “They build upon adaptive sampling ideas from [5, 8] and branch-and-bound techniques from [17].”