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courses:mapreduce-tutorial:step-15 [2012/01/25 15:46]
straka vytvořeno 		courses:mapreduce-tutorial:step-15 [2012/01/26 00:11]
straka
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-====== MapReduce Tutorial :  ======+====== MapReduce Tutorial : K-means clustering ====== 
 + 
 +Implement the [[http://en.wikipedia.org/wiki/K-means_clustering#Standard_algorithm|K-means clustering algorithm]]. You can use the following data: 
 +^ Path ^ Number of points ^ Number of dimensions ^ Number of clusters ^ 
 +| ''/home/straka/hadoop/example-inputs/points-small'' | 10000 | 50 | 50 | 
 +| ''/home/straka/hadoop/example-inputs/points-medium'' | 100000 | 100 | 100 | 
 +| ''/home/straka/hadoop/example-inputs/points-large'' | 500000 | 200 | 200 | 
 + 
 +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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