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Major clustering methods which are applied in classification of data are partition based, hierarchical (agglomerative and divisive) clustering, neural network based, density based, grid based, model based, etc. [29, 30, 31].
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Eight operation patterns have been identified and therefore the entire BAS data are partitioned into eight subsets.
During the decomposing phase, training data are partitioned into two classes in several manners and two-class learning machines are trained.
The rows of data are partitioned based on their primary key.
In fact, these data are partitioned into RSUM j = r j1 + r j2 + … + r jk parts.
In the HDFS, data are partitioned into many small chunks and each chunk has multiple backup copies.
A crucial part of developing a MapReduce application is the way input data are partitioned in order to be delivered to the required reducers.
In the first iteration, the linkage data and ranking score data are partitioned and joined before the execution of map tasks.
All data are partitioned by genomic library (i.e. EST library or BAC library sequence).
Data are partitioned into two subsets in a cross-validation analysis.
In this clustering analysis, data are partitioned into k clusters with the nearest mean.
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