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author | Timothy Hunter <timhunter@databricks.com> | 2015-12-10 12:50:46 -0800 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-12-10 12:50:46 -0800 |
commit | 2ecbe02d5b28ee562d10c1735244b90a08532c9e (patch) | |
tree | c589a01a2900513aa1b277303ed7cdffc1961ba4 /docs/mllib-frequent-pattern-mining.md | |
parent | ec5f9ed5de2218938dba52152475daafd4dc4786 (diff) | |
download | spark-2ecbe02d5b28ee562d10c1735244b90a08532c9e.tar.gz spark-2ecbe02d5b28ee562d10c1735244b90a08532c9e.tar.bz2 spark-2ecbe02d5b28ee562d10c1735244b90a08532c9e.zip |
[SPARK-12212][ML][DOC] Clarifies the difference between spark.ml, spark.mllib and mllib in the documentation.
Replaces a number of occurences of `MLlib` in the documentation that were meant to refer to the `spark.mllib` package instead. It should clarify for new users the difference between `spark.mllib` (the package) and MLlib (the umbrella project for ML in spark).
It also removes some files that I forgot to delete with #10207
Author: Timothy Hunter <timhunter@databricks.com>
Closes #10234 from thunterdb/12212.
Diffstat (limited to 'docs/mllib-frequent-pattern-mining.md')
-rw-r--r-- | docs/mllib-frequent-pattern-mining.md | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/docs/mllib-frequent-pattern-mining.md b/docs/mllib-frequent-pattern-mining.md index fe42896a05..2c8a8f2361 100644 --- a/docs/mllib-frequent-pattern-mining.md +++ b/docs/mllib-frequent-pattern-mining.md @@ -1,7 +1,7 @@ --- layout: global -title: Frequent Pattern Mining - MLlib -displayTitle: <a href="mllib-guide.html">MLlib</a> - Frequent Pattern Mining +title: Frequent Pattern Mining - spark.mllib +displayTitle: Frequent Pattern Mining - spark.mllib --- Mining frequent items, itemsets, subsequences, or other substructures is usually among the @@ -9,7 +9,7 @@ first steps to analyze a large-scale dataset, which has been an active research data mining for years. We refer users to Wikipedia's [association rule learning](http://en.wikipedia.org/wiki/Association_rule_learning) for more information. -MLlib provides a parallel implementation of FP-growth, +`spark.mllib` provides a parallel implementation of FP-growth, a popular algorithm to mining frequent itemsets. ## FP-growth @@ -22,13 +22,13 @@ Different from [Apriori-like](http://en.wikipedia.org/wiki/Apriori_algorithm) al the second step of FP-growth uses a suffix tree (FP-tree) structure to encode transactions without generating candidate sets explicitly, which are usually expensive to generate. After the second step, the frequent itemsets can be extracted from the FP-tree. -In MLlib, we implemented a parallel version of FP-growth called PFP, +In `spark.mllib`, we implemented a parallel version of FP-growth called PFP, as described in [Li et al., PFP: Parallel FP-growth for query recommendation](http://dx.doi.org/10.1145/1454008.1454027). PFP distributes the work of growing FP-trees based on the suffices of transactions, and hence more scalable than a single-machine implementation. We refer users to the papers for more details. -MLlib's FP-growth implementation takes the following (hyper-)parameters: +`spark.mllib`'s FP-growth implementation takes the following (hyper-)parameters: * `minSupport`: the minimum support for an itemset to be identified as frequent. For example, if an item appears 3 out of 5 transactions, it has a support of 3/5=0.6. @@ -126,7 +126,7 @@ PrefixSpan Approach](http://dx.doi.org/10.1109%2FTKDE.2004.77). We refer the reader to the referenced paper for formalizing the sequential pattern mining problem. -MLlib's PrefixSpan implementation takes the following parameters: +`spark.mllib`'s PrefixSpan implementation takes the following parameters: * `minSupport`: the minimum support required to be considered a frequent sequential pattern. |