Streaming-data algorithms for high-quality clustering

By Liadan O'Callaghan, Adam Meyerson, Rajeev Motwani, Nina Mishra, Sudipto Guha. Proceedings / International Conference on Data Engineering, 2002

694 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/dfeb03d7dd83d3b0ba19629a873c13c9e6cb60b8

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Citations in the corpus

A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, methodology): “Luo and Wang (2009) introduced a data streams clustering algorithm based on Grid (O'Callaghan et al. 2002; Li et al. 2009) and the PSO optimized clustering in order to get a more precise clustering efficiency .”
Density-based clustering for real-time stream data (2007, methodology): “These algorithms uses divide and conquer schemes that partition data streams into segments and discover clusters in data streams based on a k-means algorithm in finite space [10, 12].”
Closed-loop and activity-guided optogenetic control. (2015, neutral): “Various relevant streaming clustering and factor analytic approaches have already been developed (Akhtar et al., 2012; Mairal et al., 2010; O’Callaghan et al., 2002).”
The ClusTree: indexing micro-clusters for anytime stream mining (2011, methodology): “One main advantage of our approach is that we can maintain a way larger number of micro-clusters compared to other approaches [2,3,23,8] and hence the offline clustering, e.”
Online Learning: A Comprehensive Survey (2018, neutral): “(Guha et al., 2000; O’callaghan et al., 2002; Guha et al., 2003).”
Fuzzy c-Means Algorithms for Very Large Data (2012, methodology): “If the cluster center data at Line 4 are too large to be processed, it could be processed by another run of oFCM or a streaming algorithm, such as proposed in [41].”
Mining data streams: a review (2005, methodology): “The main idea behind the proposed algorithm is that it updates the cluster centers and weights after examining a batch of transactions which equalizes square root of the number of transactions rather than updating them one by one. O’Challaghan et al. [ 45 ] have proposed”
A Comprehensive Survey of Clustering Algorithms (2015, methodology): “CluStream, whichmainly deals with the shortcoming of STREAM that only describing the original data statically, regards data as a dynamic changing process.”
A survey on data stream clustering and classification (2015, methodology): “Similar to the STREAMalgorithm [75], OSKM divides text streams into small chunks, each of which can be processed effectively in main memory.”
A Framework for Projected Clustering of High Dimensional Data Streams (2004, methodology): “In [3], the authors proposed the CluStream algorithm, which has shown better clustering quality than the previously designed STREAM clustering algorithm [17].”
Maintaining variance and k-medians over data stream windows (2003, background): “There is a large body of previous work on k–median clustering [1, 5, 15, 16, 17, 18, 19].”
A Framework for Clustering Evolving Data Streams (2003, methodology): “For example, a direct extension of the stream-based k -means algorithm in [13] to such a case would require a simultaneous maintenance of the intermediate results of clustering algorithms over all possible time horizons.”
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”