PAMI: An Open-Source Python Library for Pattern Mining
Authors: Uday Kiran Rage, Veena Pamalla, Masashi Toyoda, Masaru Kitsuregawa
JMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper tackles this problem by providing a cross-platform open-source Python library called PAttern MIning (PAMI). PAMI provides several algorithms to discover different types of patterns hidden in various types of databases across multiple computing architectures. PAMI also contains algorithms to generate various types of synthetic databases. |
| Researcher Affiliation | Academia | R. Uday Kiran EMAIL The University of Aizu Aizu-Wakamatsu, Fukushima, 965-8580, Japan P. Veena EMAIL The University of Aizu Aizu-Wakamatsu, Fukushima, 965-8580, Japan Masashi Toyoda EMAIL Institute of Industrial Science, The University of Tokyo Tokyo, 153-8505, Japan Masaru Kitsuregawa EMAIL Research Organization of Information and Systems, Tokyo, 105-0001, Japan, The University of Tokyo, Tokyo, 113-8654, Japan |
| Pseudocode | No | The paper describes the architecture and usage of the PAMI library, providing conceptual explanations and code snippets for implementation, but does not include structured pseudocode or algorithm blocks for the algorithms themselves. |
| Open Source Code | Yes | Furthermore, the source code is available under the GNU General Public License, version 3. Finally, PAMI offers several resources, such as a user s guide, a developer s guide, datasets, and a bug report. Keywords: Big data, data science, data mining, machine learning, artificial intelligence, pattern mining, open-source |
| Open Datasets | Yes | We also provide over 40 large databases (Kiran, 2022) that can be used with the algorithms offered in PAMI. Furthermore, this library can be beneficial for educational purposes (say, teaching data mining courses) or evaluating algorithms performance. Finally, the website also provides information on how the output of various machine learning libraries can be pipelined into PAMI to discover needy information. |
| Dataset Splits | No | The paper describes a software library for pattern mining and provides examples of its usage. It does not present experimental results from specific datasets with defined training/test/validation splits within this paper. |
| Hardware Specification | No | The paper mentions support for "heterogeneous computing architectures (e.g., CPU-based, GPU-based, and parallel algorithms based on the mapreduce framework)", but does not provide specific details on hardware models or specifications used for any experimental evaluation within the paper. |
| Software Dependencies | No | PAMI is implemented in Python 3.6 and is cross-platform. The source code of PAMI is made available through Git Hub. The execution code of PAMI is made available through the Python Package Index (PYPI) so that users can easily install, update, or delete our library using the pip command. |
| Experiment Setup | No | The paper provides an example of how to run an algorithm with a parameter (e.g., `minSup=0.4`), but it does not present a comprehensive experimental setup or specific hyperparameters for experiments conducted within this paper. The paper focuses on describing the library itself rather than presenting new experimental results. |