Brute force Apriori algorithm implementation using Spark. This algorithm does not continue and build the association rules.
input max minsup output partitions
spark-submit \
--class "com.jgalilee.spark.apriori.JobDriver" \
--master local[4] \
./target/scala-2.10/spark-apriori_2.10-1.0.jar \
input/transactions.txt \
10 \
3 \
output \
3
- input - Path to the input transaction data.
- max - Maximum number of iterations to run for.
- minsup - Minimum support candidate itemsets to be deemed frequent itemsets.
- output - Path to write the output for iteration n - i.e. output/n
- partitions - Number of partitions to use for the transaction dataset.
Input data is assumed to be string representations in integer format -
tid \t 1 \s 2 \s ...
Where \t is a tab, and \s is a space. The number of duplicate items or order is not important. This will be resolved during the map phase.
- Spark 1.0.1
- Scala 2.10.4
The MIT License (MIT)
Copyright (c) 2014 Jack Galilee
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