LogCluster is a Perl-based tool for log file clustering and mining line patterns from log files. The development of LogCluster was inspired by SLCT, but LogCluster includes a number of novel features and data processing options.

To provide a common interface for log parsing, we write a Python wrapper around the original LogCluster source code in Perl. This also eases our benchmarking experiments. The implementation has been tested on both Linux and Windows systems. Especially, Strawberry Perl has been installed to run the Perl program on Windows.

Read more information about LogCluster from the following paper:

• Risto Vaarandi, Mauno Pihelgas. LogCluster - A Data Clustering and Pattern Mining Algorithm for Event Logs, Proceedings of the 11th International Conference on Network and Service Management (CNSM), 2015.

The code has been tested in the following enviornment:

• python 3.7.6
• regex 2022.3.2
• pandas 1.0.1
• numpy 1.18.1
• scipy 1.4.1
• perl 5.26.1

Run the following scripts to start the demo:

python demo.py

Run the following scripts to execute the benchmark:

python benchmark.py

Running the benchmark script on Loghub_2k datasets, you could obtain the following results.

🔭 If you use our logparser tools or benchmarking results in your publication, please kindly cite the following papers.

• [ICSE'19] Jieming Zhu, Shilin He, Jinyang Liu, Pinjia He, Qi Xie, Zibin Zheng, Michael R. Lyu. Tools and Benchmarks for Automated Log Parsing. International Conference on Software Engineering (ICSE), 2019.
• [DSN'16] Pinjia He, Jieming Zhu, Shilin He, Jian Li, Michael R. Lyu. An Evaluation Study on Log Parsing and Its Use in Log Mining. IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2016.