Parallel K-Means Clustering Based on MapReduce

By Weizhong Zhao, Huifang Ma, Qing He. International Conference on Cloud Computing, 2009

706 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Web-Scale Image Clustering Revisited (2015, methodology): “Parallelism has widely been utilized for large scale clustering [24, 15], while algorithms for distributed systems exist for many popular clustering algorithms like Parallel kmeans [39] or parallel DB-SCAN [25].”
Making AI Forget You: Data Deletion in Machine Learning (2019, methodology): “Our method has close similarities to pre-existing distributed k -means algorithms [69, 67, 9, 7, 39, 8, 92], but is in fact distinct (not only in that it is modiﬁed for deletion, but also in that it operates over general rooted trees).”
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines (2021, methodology): “Over the past few years, variants of k -means clustering using MapReduce [96]–[98] have been introduced.”
Scalable K-Means++ (2012, methodology): “[12] showed how to implement EM (a generalization of k-means) in MapReduce; see also [36] who used similar tricks to speed up k-means.”
A Comprehensive Survey of Clustering Algorithms (2015, background): “The main basic ideas of clustering for big data can be summarized in the following 4 categories: (1) sample clustering [10,18]; (2) data merged clustering [17,134]; (3) dimension-reducing clustering [135,136]; (4) parallel clustering [114,137–139]; Typical algorithms of this kind of clustering areK-”