A Framework for Clustering Evolving Data Streams

By Charu C. Aggarwal, Jiawei Han, Jianyong Wang, Philip S. Yu. 2003

2,052 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, methodology): “3) CluStream: The stream clustering approach, CluS-tream [3], uses the cluster feature vector structure [75] for maintaining sets of micro-clusters online.”
Online Machine Learning in Big Data Streams (2018, methodology): “For clustering, it includes an algorithm based on CluStream [9] (Section 6.1).”
Fuzzy c-Means Algorithms for Very Large Data (2012, background): “Representative algorithms include incremental clustering [23], [24] and divide-and-conquer approaches [25], [26].”
Density-Based Clustering over an Evolving Data Stream with Noise (2006, methodology): “The data stream for mining often exists over months or years, and the underlying model often changes (known as evolution) during this time [1, 18].”
Density-based Projected Clustering over High Dimensional Data Streams (2012, methodology): “A usual way to overcome this problem is by summarizing the data through an appropriate summary structure e.g., [1,2,7,8].”
Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007, methodology): “In the field of data stream mining, Aggarwal, Han, Wang, and Yu (2003) proposed a data selection method based on the pyramidal time frame to test the hypothesis that the more recent data would reflect user access patterns more accurately.”
Density-based clustering for real-time stream data (2007, methodology): “Like CluStream [1], DStream has an online component and an offline component.”
Adaptive random forests for evolving data stream classification (2017, background): “attack or common access, an inherent streaming scenario since instances are sequentially presented as a time series [3].”
Evolutionary clustering (2006, methodology): “[1] study the problem of clustering data streams at different time horizons using an online statistical aggregation stage and then an offline clustering stage.”
Trajectory Data Mining (2015, methodology): “Both Lee [2007] and Li [2010] adopted a Micro-and-Macroclustering framework , which was proposed by Aggarwal et al. [2003] to cluster data streams.”
Enabling Fast Lazy Learning for Data Streams (2011, background): “Though inspired by micro-clusters [2], exemplars are different and more complex in that they summarize labeled data.”
A Survey of Uncertain Data Algorithms and Applications (2009, background): “In order to do so, the microclustering concept developed in [11] is extended to the uncertain case.”
Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R (2017, methodology): “Examples are Very Fast Decision Trees (VFDT) (Domingos and Hulten 2000) using Hoeffding trees, the time window-based Online Information Network (OLIN) (Last 2002) and On-demand Classification (Aggarwal, Han, Wang, and Yu 2004) based on micro-clusters found with the data-stream clustering algorithm C”
Scalable real-time classification of data streams with concept drift (2017, methodology): “To overcome the computational bottleneck of real-time KNN and the problems associated with the slidingwindow, the presented classifier adaptsMicro-Clusters [37] — a technique originally developed for data stream clustering [24] in order to provide a summary of the locality of the data are of the for”