Name
Abstract | ||
|---|---|---|
MapReduce is a popular model in which the dataflow takes the form of a directed acyclic graph of operators. But it lacks built-in support for iterative programs, which arise naturally in many clustering applications. Based on micro-cluster and equivalence relation, we design a clustering algorithm which can be easily parallelized in MapReduce and done in quite a few MapReduce rounds. Experiments show that our algorithm not only runs fast and obtains good accuracy but also scales well and possesses high speedup. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39065-4_64 | ISNN (1) |
Keywords | Field | DocType |
clustering application,obtains good accuracy,acyclic graph,popular model,equivalence relation,fast algorithm,high speedup,clustering algorithm,built-in support,mapreduce round,iterative program,clustering,data mining | Equivalence relation,Computer science,Algorithm,Theoretical computer science,Directed acyclic graph,Dataflow,Operator (computer programming),Cluster analysis,Micro cluster,Speedup | Conference |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuqing Miao | 1 | 4 | 2.48 |
| Jinxing Zhang | 2 | 0 | 0.34 |
| Hao Feng | 3 | 409 | 32.15 |
| Liangpei Qiu | 4 | 2 | 0.72 |
| Yimin Wen | 5 | 18 | 3.24 |