BIRCH: an efficient data clustering method for very large databases

By Tian Zhang, Raghu Ramakrishnan, Miron Livny. 1996

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Citations in the corpus

Is data clustering in adversarial settings secure? (2013, methodology): “(c) We use the single-linkage hierarchical clustering instead of the BIRCH algorithm [30], since this modification does not significantly affect the quality of the clustering results, as the authors demonstrated in [23].”
Accelerating Protein Design Using Autoregressive Generative Models (2019, methodology): “We then used BIRCH clustering (Zhang et al., 1996) to find diverse members of the dataset in O(n) time.”
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.”
Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input (2018, methodology): “We concatenate the image and speech vectors for each component, and ﬁnally perform Birch clustering [58] with 1000 target clusters for the ﬁrst step, and an agglomerative ﬁnal step that resulted in 135 clusters.”
DATA MINING TECHNIQUES (2003, methodology): “Some clustering algorithms like BIRCH [85] also have a step to detect outliers.”
WaveCluster: a wavelet-based clustering approach for spatial data in very large databases (2000, methodology): “Tests were done on synthetic datasets generated by us and also on datasets used to evaluate BIRCH [ZRL96”
Incremental OPTICS: Efficient Computation of Updates in a Hierarchical Cluster Ordering (2003, background): “[1,2,3,4,5]), the problem of incrementally updating mined patterns is a rather new area of research.”
StreamKM++: A clustering algorithm for data streams (2010, methodology): “We compare our algorithm with algorithms BIRCH [Zhang et al. 1996] and STREAMLS [O Callaghan et al. 2002; Guha et al. 2003], which are both frequently used to cluster data streams.”
On Discovering Moving Clusters in Spatio-temporal Data (2005, methodology): “Algorithms like BIRCH [8] and CURE [9] were proposed to improve the scalability of agglomerative clustering and the quality of the discovered partitions.”
Indexing the Distance: An Efficient Method to KNN Processing (2001, methodology): “There are several existing clustering schemes in the literature such as BIRCH [20] and CURE [10].”
Data mining in clinical big data: the frequently used databases, steps, and methodological models (2021, methodology): “Common algorithms for hierarchical clustering include BIRCH [72], CURE [73], and ROCK [74].”
Scalable K-Means++ (2012, methodology): “A paper on database clustering [35] won the 2006 SIGMOD Test of Time Award.”
Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms (2004, methodology): “The next section describes evaluation graphs, followed by a description of the L method.”