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Diffstat (limited to 'docs/mllib-frequent-pattern-mining.md')
-rw-r--r-- | docs/mllib-frequent-pattern-mining.md | 13 |
1 files changed, 13 insertions, 0 deletions
diff --git a/docs/mllib-frequent-pattern-mining.md b/docs/mllib-frequent-pattern-mining.md index 4d4f5cfdc5..f749eb4f2f 100644 --- a/docs/mllib-frequent-pattern-mining.md +++ b/docs/mllib-frequent-pattern-mining.md @@ -50,6 +50,7 @@ example illustrates how to mine frequent itemsets and association rules Rules](mllib-frequent-pattern-mining.html#association-rules) for details) from `transactions`. +Refer to the [`FPGrowth` Scala docs](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth) for details on the API. {% highlight scala %} import org.apache.spark.rdd.RDD @@ -92,6 +93,8 @@ example illustrates how to mine frequent itemsets and association rules Rules](mllib-frequent-pattern-mining.html#association-rules) for details) from `transactions`. +Refer to the [`FPGrowth` Java docs](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) for details on the API. + {% highlight java %} import java.util.Arrays; import java.util.List; @@ -144,6 +147,8 @@ Calling `FPGrowth.train` with transactions returns an [`FPGrowthModel`](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowthModel) that stores the frequent itemsets with their frequencies. +Refer to the [`FPGrowth` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowth) for more details on the API. + {% highlight python %} from pyspark.mllib.fpm import FPGrowth @@ -170,6 +175,8 @@ for fi in result: implements a parallel rule generation algorithm for constructing rules that have a single item as the consequent. +Refer to the [`AssociationRules` Scala docs](api/java/org/apache/spark/mllib/fpm/AssociationRules.html) for details on the API. + {% highlight scala %} import org.apache.spark.rdd.RDD import org.apache.spark.mllib.fpm.AssociationRules @@ -199,6 +206,8 @@ results.collect().foreach { rule => implements a parallel rule generation algorithm for constructing rules that have a single item as the consequent. +Refer to the [`AssociationRules` Java docs](api/java/org/apache/spark/mllib/fpm/AssociationRules.html) for details on the API. + {% highlight java %} import java.util.Arrays; @@ -267,6 +276,8 @@ Calling `PrefixSpan.run` returns a [`PrefixSpanModel`](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpanModel) that stores the frequent sequences with their frequencies. +Refer to the [`PrefixSpan` Scala docs](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpan) and [`PrefixSpanModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpanModel) for details on the API. + {% highlight scala %} import org.apache.spark.mllib.fpm.PrefixSpan @@ -296,6 +307,8 @@ Calling `PrefixSpan.run` returns a [`PrefixSpanModel`](api/java/org/apache/spark/mllib/fpm/PrefixSpanModel.html) that stores the frequent sequences with their frequencies. +Refer to the [`PrefixSpan` Java docs](api/java/org/apache/spark/mllib/fpm/PrefixSpan.html) and [`PrefixSpanModel` Java docs](api/java/org/apache/spark/mllib/fpm/PrefixSpanModel.html) for details on the API. + {% highlight java %} import java.util.Arrays; import java.util.List; |