A Framework for Projected Clustering of High Dimensional Data Streams

By Charu C. Aggarwal, Jiawei Han, Jianyong Wang, Philip S. Yu. 2004

573 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): “The reservoir sampling, queue, Online k -means, HPStream, no buffer, full rehearsal, and ofﬂine DNN baselines were implemented in Python 3.6.”
Tracking recurring contexts using ensemble classifiers: an application to email filtering (2009, methodology): “Well known representatives of streaming clusterers are SWClustering [16], STREAM [17], HPStream [18] and BIRCH [19].”
A Comprehensive Survey of Clustering Algorithms (2015, methodology): “Time complexity (Table 21): The time complexity of CluStream, HPStream and DenStream is involved in the online and offline processes.”
Density-Based Clustering over an Evolving Data Stream with Noise (2006, methodology): “Analyzing and mining such kinds of data have been becoming a hot topic [1, 2, 4, 6, 10, 14].”
Data Streams: Models and Algorithms (Advances in Database Systems) (2006, methodology): “[3] Aggarwal C., Han J., Wang J., Yu P. S., (2004) On Demand Classification of Data Streams, Proc.”
Density-Based Projected Clustering of Data Streams (2012, methodology): “To evaluate the quality of the clustering results, the cluster purity measure [3,7] is used.”
Clustering Data Streams Based on Shared Density between Micro-Clusters (2016, methodology): “For high-dimensional data we plan to extend a subspace clustering approach like HPStream [15] to maintain a shared density graph in lower-dimensional subspaces.”
Progress in Outlier Detection Techniques: A Survey (2019, methodology): “Grid-based clustering methods: STING [94], Wavecluster [155], DCluster [156] VOLUME 7, 2019 107983 v. Clustering methods for High-Dimensional Data: CLIQUE [157], HPStream [158] In addition to the following algorithms, which have been covered in existing literature [5], [22], [26], [33], a two-phase ”
Mining data streams: a review (2005, background): “They [2] have recently proposed HPStream; a projected clustering for high dimensional data streams.”
A survey on data stream clustering and classification (2015, methodology): “HPStream addresses the problem of high-dimensional data streams by deploying a projection technique to select the best attribute set for each cluster (subspace clustering) [4].”
Feature Selection for Clustering: A Review (2018, methodology): “The proposed method in [4] is called HPStream.”
Online Learning: A Comprehensive Survey (2018, background): “een developed for online clustering for very high dimensional data where the data sparsity makes it very hard to perform clustering as many instances tend to be equidistant from one another. HPStream(Aggarwal et al., 2004) introduces a concept of projected clustering to data streams. There are onlin”
Density-based Projected Clustering over High Dimensional Data Streams (2012, methodology): “This is in contrast to HPStream [2], where a new point, if it is far away from all existing clusters, will become the seed of a new cluster (even if it is an outlier), and some old cluster is deleted in order to keep the total number of clusters constant.”
Outlier Analysis (2013, background): “This is because many of these algorithms report outliers as side products of the clustering algorithm [6, 111].”
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.”