Apache Spark reassigned SPARK-20114:

    Assignee:     (was: Apache Spark)

> spark.ml parity for sequential pattern mining - PrefixSpan
> ----------------------------------------------------------
>                 Key: SPARK-20114
>                 URL: https://issues.apache.org/jira/browse/SPARK-20114
>             Project: Spark
>          Issue Type: Sub-task
>          Components: ML
>    Affects Versions: 2.2.0
>            Reporter: yuhao yang
>            Priority: Major
> Creating this jira to track the feature parity for PrefixSpan and sequential 
> pattern mining in Spark ml with DataFrame API. 
> First list a few design issues to be discussed, then subtasks like Scala, 
> Python and R API will be created.
> # Wrapping the MLlib PrefixSpan and provide a generic fit() should be 
> straightforward. Yet PrefixSpan only extracts frequent sequential patterns, 
> which is not good to be used directly for predicting on new records. Please 
> read  
> http://data-mining.philippe-fournier-viger.com/introduction-to-sequential-rule-mining/
>  for some background knowledge. Thanks Philippe Fournier-Viger for providing 
> insights. If we want to keep using the Estimator/Transformer pattern, options 
> are:
>      #*  Implement a dummy transform for PrefixSpanModel, which will not add 
> new column to the input DataSet. The PrefixSpanModel is only used to provide 
> access for frequent sequential patterns.
>      #*  Adding the feature to extract sequential rules from sequential 
> patterns. Then use the sequential rules in the transform as FPGrowthModel.  
> The rules extracted are of the form X–> Y where X and Y are sequential 
> patterns. But in practice, these rules are not very good as they are too 
> precise and thus not noise tolerant.
> #  Different from association rules and frequent itemsets, sequential rules 
> can be extracted from the original dataset more efficiently using algorithms 
> like RuleGrowth, ERMiner. The rules are X–> Y where X is unordered and Y is 
> unordered, but X must appear before Y, which is more general and can work 
> better in practice for prediction. 
> I'd like to hear more from the users to see which kind of Sequential rules 
> are more practical. 

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