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https://issues.apache.org/jira/browse/SPARK-20114?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15944239#comment-15944239
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yuhao yang commented on SPARK-20114:
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Currently I prefer to implement the dummy PrefixSpanModel as the sequential
rules extracted won't be quite useful.
> spark.ml parity for sequential pattern mining - PrefixSpan
> ----------------------------------------------------------
>
> Key: SPARK-20114
> URL: https://issues.apache.org/jira/browse/SPARK-20114
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Affects Versions: 2.2.0
> Reporter: yuhao yang
>
> 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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