RuleMiner.jl - Association Rule Mining in Julia
About
RuleMiner.jl is a Julia package for association rule and frequent itemset mining inspired by the arules R package and SPMF Java library.
Key features of RuleMiner.jl include:
- Support for Julia's native multithreading capabilities for improved performance
- Direct interfaces with DataFrames.jl for loading transactional data and exporting results
- Flexible handling of either relative (percentage) support or absolute (count) support in minimum support thresholds
Algorithms
The package currently has support for these algorithms:
Association Rule Mining
- A Priori[^1]
Frequent Itemset Mining
- ECLAT[^2]
- FP-Growth[^3]
Closed Itemset Mining
- FPClose[^4]
- CHARM[^5]
- LCM[^6]
- CARPENTER[^7]
- LevelWise[^8] (for recovering frequent itemsets)
Installation
julia> ]
pkg> add RuleMinerUsage
These examples use the retail dataset from the Frequent Itemset Mining Implementenations (FIMI) repository hosted by the University of Antwerp.
using RuleMinerLoad data to create a Transactions object or alternatively convert an existing 1-hot encoded DataFrame.
data = load_transactions("retail.txt",' ')
data = transactions(df)Generate association rules using A Priori with 10% minimum support and a max rule length of 3.
arules = apriori(data, 0.1, 3)Result:
13×8 DataFrame
Row │ LHS RHS Support Confidence Coverage Lift N Length
│ Array… String Float64 Float64 Float64 Float64 Int64 Int64
─────┼────────────────────────────────────────────────────────────────────────────
1 │ String[] 33 0.172036 0.172036 1.0 1.0 15167 1
2 │ String[] 39 0.176902 0.176902 1.0 1.0 15596 1
3 │ String[] 40 0.574794 0.574794 1.0 1.0 50675 1
4 │ String[] 42 0.169517 0.169517 1.0 1.0 14945 1
5 │ String[] 49 0.477927 0.477927 1.0 1.0 42135 1
6 │ ["40"] 42 0.129466 0.225239 0.574794 2482.19 11414 2
7 │ ["49"] 42 0.102289 0.214026 0.477927 2358.62 9018 2
8 │ ["39"] 40 0.117341 0.663311 0.176902 106.519 10345 2
9 │ ["40"] 49 0.330551 0.575076 0.574794 2668.42 29142 2
10 │ ["49"] 40 0.330551 0.691634 0.477927 111.067 29142 2
11 │ ["42"] 49 0.102289 0.603413 0.169517 2799.9 9018 2
12 │ ["40"] 39 0.117341 0.204144 0.574794 67.6607 10345 2
13 │ ["42"] 40 0.129466 0.763734 0.169517 122.645 11414 2Generate frequent itemsets with a minimum support of 5,000 transactions using ECLAT
itemsets = eclat(data, 5000)Result:
15×4 DataFrame
Row │ Itemset Support N Length
│ Array… Float64 Int64 Int64
─────┼──────────────────────────────────────────────
1 │ ["42"] 0.169517 14945 1
2 │ ["33"] 0.172036 15167 1
3 │ ["39"] 0.176902 15596 1
4 │ ["49"] 0.477927 42135 1
5 │ ["40"] 0.574794 50675 1
6 │ ["39", "49"] 0.0901068 7944 2
7 │ ["49", "40"] 0.330551 29142 2
8 │ ["39", "49", "40"] 0.0692135 6102 3
9 │ ["39", "40"] 0.117341 10345 2
10 │ ["42", "49"] 0.102289 9018 2
11 │ ["42", "49", "40"] 0.0835507 7366 3
12 │ ["42", "40"] 0.129466 11414 2
13 │ ["33", "49"] 0.0911277 8034 2
14 │ ["33", "49", "40"] 0.0612736 5402 3
15 │ ["33", "40"] 0.095903 8455 2Multithreading
RuleMiner.jl makes use of Julia's native multithreading support for significant performance gains. Enabling multithreading is done by using the -t flag when launching Julia and either specifying the number of threads or passing in the auto argument to launch julia with all available threads.
$ julia -t autoOnce Julia is launched, you can can view the enabled threads with nthreads() from the Base.Threads module.
julia> using Base.Threads
julia> nthreads()See this post for more info on multithreading in Julia.
[!TIP] Multithreading can be configured for the VScode integrated terminal by setting the
julia.NumThreadsparameter in VScode settings.
Future Work
RuleMiner 0.4.0 is planned to support maximal itemset mining.
References
[^1]: Agrawal, Rakesh, and Ramakrishnan Srikant. “Fast Algorithms for Mining Association Rules in Large Databases.” In Proceedings of the 20th International Conference on Very Large Data Bases, 487–99. VLDB ’94. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1994.
[^2]: Zaki, Mohammed. “Scalable Algorithms for Association Mining.” Knowledge and Data Engineering, IEEE Transactions On 12 (June 1, 2000): 372–90. https://doi.org/10.1109/69.846291.
[^3]: Han, Jiawei, Jian Pei, and Yiwen Yin. “Mining Frequent Patterns without Candidate Generation.” SIGMOD Rec. 29, no. 2 (May 16, 2000): 1–12. https://doi.org/10.1145/335191.335372.
[^4]: Grahne, Gösta, and Jianfei Zhu. “Fast Algorithms for Frequent Itemset Mining Using FP-Trees.” IEEE Transactions on Knowledge and Data Engineering 17, no. 10 (October 2005): 1347–62. https://doi.org/10.1109/TKDE.2005.166.
[^5]: Zaki, Mohammed, and Ching-Jui Hsiao. “CHARM: An Efficient Algorithm for Closed Itemset Mining.” In Proceedings of the 2002 SIAM International Conference on Data Mining (SDM), 457–73. Proceedings. Society for Industrial and Applied Mathematics, 2002. https://doi.org/10.1137/1.9781611972726.27.
[^6]: Uno, Takeaki, Tatsuya Asai, Yuzo Uchida, and Hiroki Arimura. “An Efficient Algorithm for Enumerating Closed Patterns in Transaction Databases.” In Discovery Science, edited by Einoshin Suzuki and Setsuo Arikawa, 16–31. Berlin, Heidelberg: Springer, 2004. https://doi.org/10.1007/978-3-540-30214-8_2.
[^7]: Pan, Feng, Gao Cong, Anthony K. H. Tung, Jiong Yang, and Mohammed J. Zaki. “Carpenter: Finding Closed Patterns in Long Biological Datasets.” In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 637–42. KDD ’03. New York, NY, USA: Association for Computing Machinery, 2003. https://doi.org/10.1145/956750.956832.
[^8]: Pasquier, Nicolas, Yves Bastide, Rafik Taouil, and Lotfi Lakhal. “Efficient Mining of Association Rules Using Closed Itemset Lattices.” Information Systems 24, no. 1 (March 1, 1999): 25–46. https://doi.org/10.1016/S0306-4379(99)00003-4.