Hari Shrawgi
Papers
1
Total Citations
4
H-Index
1
About
Hari Shrawgi is a researcher focused on the intersection of data mining, web analytics, and clustering algorithms. His work addresses the critical challenge of analyzing large-scale web robot session data, where traditional clustering techniques often falter due to scale and noise. In his most-cited paper, "Performance Evaluation of Large Data Clustering Techniques on Web Robot Session Data" (2018), Shrawgi systematically benchmarks various clustering methods—such as k-means, hierarchical, and density-based approaches—on real-world web session logs. This study provides a practical framework for distinguishing human browsing behavior from automated bot traffic, a key concern in cybersecurity and web traffic analysis. While his citation count of 4 reflects a niche but growing interest in this specialized area, his contributions are foundational for researchers optimizing bot detection and session clustering at scale. Shrawgi’s work is particularly valuable for students and practitioners seeking robust, data-driven methods to handle high-dimensional, imbalanced datasets in web analytics.
Research Focus
Key Achievements
Top Papers
- 1