2009 International Conference on Parallel and Distributed Computing, Applications and Technologies 2009
DOI: 10.1109/pdcat.2009.46
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An Efficient Hierarchical Clustering Method for Large Datasets with Map-Reduce

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Cited by 40 publications
(24 citation statements)
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“…In this study, we catch these center regions by using different clustering algorithms ( -means [28,29], density-based spatial clustering of applications with noise (DBSCAN) [30,31], and balanced iterative reducing and clustering using hierarchies (BIRCH) [32,33]). …”
Section: Scenario Demonstrationmentioning
confidence: 99%
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“…In this study, we catch these center regions by using different clustering algorithms ( -means [28,29], density-based spatial clustering of applications with noise (DBSCAN) [30,31], and balanced iterative reducing and clustering using hierarchies (BIRCH) [32,33]). …”
Section: Scenario Demonstrationmentioning
confidence: 99%
“…BIRCH is a clustering algorithm based on hierarchy [32]. This algorithm uses two concepts, namely, clustering feature and clustering feature tree, to generalize clustering description [33].…”
Section: Basic K-meansmentioning
confidence: 99%
“…There is a variety of MapReduce-based implementations of hierarchical clustering methods available, such as those in [48,49,50,51,52]. Two of them are described following.…”
Section: Hierarchical Clustering Approaches Implemented In the Mapredmentioning
confidence: 99%
“…The proposed algorithm in [49] has two main phases in the context of clustering users of internet web logs. The first phase is a feature-selection step, which improves the efficiency of hierarchical clustering by reducing the dimension of data.…”
Section: Hierarchical Clustering Approaches Implemented In the Mapredmentioning
confidence: 99%
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