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Both sides previous revision Previous revision 2012/01/29 16:40 straka 2012/01/28 23:23 majlis Added links to previous and next chapter.2012/01/26 23:19 straka 2012/01/26 00:11 straka 2012/01/26 00:06 straka 2012/01/25 22:20 straka 2012/01/25 15:46 straka vytvořeno Go		2012/01/29 16:40 straka 2012/01/28 23:23 majlis Added links to previous and next chapter.2012/01/26 23:19 straka 2012/01/26 00:11 straka 2012/01/26 00:06 straka 2012/01/25 22:20 straka 2012/01/25 15:46 straka vytvořeno Go Next revision Both sides next revision
courses:mapreduce-tutorial:step-15 [2012/01/26 00:06] straka		courses:mapreduce-tutorial:step-15 [2012/01/26 00:11] straka
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	| ''/home/straka/hadoop/example-inputs/points-medium'' | 100000 | 100 | 100 |		| ''/home/straka/hadoop/example-inputs/points-medium'' | 100000 | 100 | 100 |
	| ''/home/straka/hadoop/example-inputs/points-large'' | 500000 | 200 | 200 |		| ''/home/straka/hadoop/example-inputs/points-large'' | 500000 | 200 | 200 |
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		+	When dealing with iterative algorithms, each iteration is usually implemented as one Hadoop job. The Hadoop input_path contains the input data and each mapper also reads the current clusters. The reducers are used to aggregate the data and output new cluster centers. A controlling script is taking care of executing Hadoop jobs and stopping the iteration when the algorithm converges.
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