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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// scalastyle:off println
package org.apache.spark.examples.mllib
import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.fpm.AssociationRules
import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
// $example off$
object AssociationRulesExample {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("AssociationRulesExample")
val sc = new SparkContext(conf)
// $example on$
val freqItemsets = sc.parallelize(Seq(
new FreqItemset(Array("a"), 15L),
new FreqItemset(Array("b"), 35L),
new FreqItemset(Array("a", "b"), 12L)
))
val ar = new AssociationRules()
.setMinConfidence(0.8)
val results = ar.run(freqItemsets)
results.collect().foreach { rule =>
println("[" + rule.antecedent.mkString(",")
+ "=>"
+ rule.consequent.mkString(",") + "]," + rule.confidence)
}
// $example off$
}
}
// scalastyle:on println
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