Rahul Borkar
Papers
1
Total Citations
4
H-Index
1
About
Rahul Borkar’s research focuses on the intersection of data mining, web analytics, and machine learning, with a particular emphasis on understanding and improving the performance of clustering techniques for large-scale web data. His most cited work, “Performance Evaluation of Large Data Clustering Techniques on Web Robot Session Data” (2018), systematically compares clustering algorithms on real-world web robot session logs—a critical area for distinguishing human users from automated bots. This study provides actionable insights for optimizing web traffic analysis and cybersecurity, offering a benchmark for future algorithm selection. While his citation count is modest, the work’s practical relevance to web data preprocessing and anomaly detection underscores its value for researchers tackling big data challenges in web intelligence. Borkar’s contributions are particularly notable for bridging the gap between theoretical clustering methods and their application to noisy, high-volume web data, making his findings directly useful for industry practitioners and academics alike. His research serves as a foundation for more efficient web robot detection and session analysis, highlighting the importance of tailored evaluation metrics in data-driven web environments.
Research Focus
Key Achievements
Top Papers
- 1