Package org.apache.spark.ml.fpm
Interface FPGrowthParams
- All Superinterfaces:
- HasPredictionCol,- Identifiable,- Params,- Serializable
- All Known Implementing Classes:
- FPGrowth,- FPGrowthModel
Common params for FPGrowth and FPGrowthModel
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Method SummaryModifier and TypeMethodDescriptiondoubledoubleintitemsCol()Items column name.Minimal confidence for generating Association Rule.Minimal support level of the frequent pattern.Number of partitions (at least 1) used by parallel FP-growth.validateAndTransformSchema(StructType schema) Validates and transforms the input schema.Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionColgetPredictionCol, predictionColMethods inherited from interface org.apache.spark.ml.util.IdentifiabletoString, uidMethods inherited from interface org.apache.spark.ml.param.Paramsclear, copy, copyValues, defaultCopy, defaultParamMap, estimateMatadataSize, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
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Method Details- 
getItemsColString getItemsCol()
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getMinConfidencedouble getMinConfidence()
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getMinSupportdouble getMinSupport()
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getNumPartitionsint getNumPartitions()
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itemsColItems column name. Default: "items"- Returns:
- (undocumented)
 
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minConfidenceDoubleParam minConfidence()Minimal confidence for generating Association Rule. minConfidence will not affect the mining for frequent itemsets, but will affect the association rules generation. Default: 0.8- Returns:
- (undocumented)
 
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minSupportDoubleParam minSupport()Minimal support level of the frequent pattern. [0.0, 1.0]. Any pattern that appears more than (minSupport * size-of-the-dataset) times will be output in the frequent itemsets. Default: 0.3- Returns:
- (undocumented)
 
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numPartitionsIntParam numPartitions()Number of partitions (at least 1) used by parallel FP-growth. By default the param is not set, and partition number of the input dataset is used.- Returns:
- (undocumented)
 
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validateAndTransformSchemaValidates and transforms the input schema.- Parameters:
- schema- input schema
- Returns:
- output schema
 
 
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