BIRCH: A New Data Clustering Algorithm and Its Applications

By Tian Zhang, Raghu Ramakrishnan, Miron Livny. 1997

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Citations in the corpus

Memory Efficient Experience Replay for Streaming Learning (2018, neutral): “global clustering (macro-clusters) [3], [5], [22], [45], [67], [75].”
Fast Agglomerative Clustering Using a k-Nearest Neighbor Graph (2006, methodology): “We consider three image data sets from [3], three BIRCH data sets [23], and five high dimensional data sets Dim64 to Dim1024 with higher dimensionality varying from 64 to 1,024.”
Improving K-Means by Outlier Removal (2005, methodology): “In outlier detection methods based on clustering, outlier is defined to be an observation that does not fit to the overall clustering pattern [6].”
StreamKM++: A clustering algorithm for data streams (2010, methodology): “We compare our algorithm experimentally with two well-known streaming implementations (BIRCH [16] and StreamLS [4, 9]).”
Learning Binary Trees via Sparse Relaxation (2020, background): “Divisive : a ‘top-down’ approach that starts with one cluster and recusively splits clusters (Zhang et al., 1997; Widyantoro et al., 2002; Krishnamurthy et al., 2012; Dasgupta, 2016; Kobren et al., 2017; Moseley & Wang, 2017).”
Computing LTS Regression for Large Data Sets (2005, background): “Recently improved versions of CLARA for very large databases were developed, such as CLARANS (Ng and Han, 1994); see also Zhang et al. (1997) and Huang (1998) and the references therein.”
QuickBundles, a Method for Tractography Simplification (2012, methodology): “Unlike amalgamation clustering algorithms such as k-means (Steinhaus, 1956; MacQueen, 1967) or BIRCH (Zhang et al., 1997), there is no reassignment or updating phase in QB – once an item is assigned to a cluster it stays there, and clusters are not amalgamated.”
Alternatives to the k-means algorithm that find better clusterings (2002, methodology): “Our experiments use two datasets already used in recent empirical work on clustering algorithms [23, 14], and a photograph of a hand from [4].”
A Survey of Clustering Data Mining Techniques (2006, methodology): “The pivotal role here belongs to the algorithm BIRCH (Balanced Iterative Reduction and Clustering using Hierarchies) [Zhang et al. 1996; Zhang et al. 1997].”
An Efficient k-Means Clustering Algorithm: Analysis and Implementation (2002, methodology): “These studies, as well as a comparison we ran against the popular clustering scheme, BIRCH 1 [50], are reported in Section4.”
Data Mining for the Internet of Things: Literature Review and Challenges (2015, unknown): “WKPDS ENNS EENNS EEENNS Bayesian network Bayesian multinets SVM GSVM FSVM TWSVMs VaR-SVM RSVM One- Bayesian dependence Naïve Bayes Selective naïve Bayes Bayes Seminaïve k-dependence Bayesian Clustering Hierarchical Agglomerative CURE Divisive SVD Partitioning SNOB MCLUST DBSCAN BANG Cooccurrence ROC”
Outlier Detection Using k-Nearest Neighbour Graph (2004, methodology): “In clustering-based methods, outlier is defined to be observation that does not fit to the overall clustering pattern [15].”