International Journal of Research and Scientific Innovation (IJRSI)
Comparative Analysis of Data Mining Tools: Performance, Scalability, and Usability in the AI Era
Published May 12, 2026 • Vol. 13, Issue 4, pp. 1986–1988Open Access
DOI: 10.51244/IJRSI.2026.1304000167
Abstract
In the 2026 data landscape, the volume of unstructured data and the demand for real-time insights have redefined the requirements for data mining tools. This paper evaluates six leading tools—RapidMiner, KNIME, Weka, Orange, Python (Scikit-Learn), and Apache Spark (MLlib)—across four critical dimensions: algorithmic diversity, computational efficiency, ease of deployment, and integration with modern cloud-native architectures. Our findings suggest a distinct bifurcation between "low-code" platforms for rapid business deployment and "pro-code" environments for high-scale, custom algorithmic development.
Keywords: data mining, Generative AI, Data mining Tools
| Journal | International Journal of Research and Scientific Innovation (IJRSI) |
|---|---|
| ISSN | 2321-2705 |
| Volume / Issue | Volume 13, Issue 4 |
| Pages | 1986–1988 |
| Publication date | May 12, 2026 |
| DOI | 10.51244/IJRSI.2026.1304000167 |
| Publisher | RSIS International |
| License | Open Access |
How to cite this article
Dr. Het Trivedi, & Mrs. Komal Trivedi (2026). Comparative Analysis of Data Mining Tools: Performance, Scalability, and Usability in the AI Era. International Journal of Research and Scientific Innovation (IJRSI), 13(4), 1986-1988. https://doi.org/10.51244/IJRSI.2026.1304000167
BibTeX
@article{Dr2026,
title = {Comparative Analysis of Data Mining Tools: Performance, Scalability, and Usability in the AI Era},
author = {Dr. Het Trivedi and Mrs. Komal Trivedi},
journal = {International Journal of Research and Scientific Innovation (IJRSI)},
volume = {13},
number = {4},
pages = {1986--1988},
year = {2026},
doi = {10.51244/IJRSI.2026.1304000167},
publisher = {RSIS International}
}