World's Best Scientists 2026 revealed!
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Engineering and Technology
USA
2026

D-Index & Metrics

Engineering and Technology

D-Index
119
Citations
165786
World Ranking
51
National Ranking
19

Research.com Recognitions

  • 2026 - Research.com Engineering and Technology in United States Leader Award
  • 2025 - Research.com Engineering and Technology in United States Leader Award
  • 2022 - Research.com Engineering and Technology in United States Leader Award
  • Fellow of the Indian National Academy of Engineering (INAE)
  • Fellow of the Indian National Academy of Engineering (INAE)
  • Fellow of the Indian National Academy of Engineering (INAE)

Overview

Rakesh Agrawal is affiliated with Purdue University West Lafayette in the United States. Their research spans multiple domains, primarily centered around medicine and business-related areas.

Their recent scholarly output includes three papers published between 2022 and 2024. These papers cover diverse topics ranging from medical procedures to financial literacy:

  • Profiling of OTT Viewers and their Perception Mapping using Advanced Technological Models, 2024, International Journal of Religion
  • Comparison of Craniotomy and Stereotactic Aspiration Plus Thrombolysis in Isolated Capsulo-Ganglionic Hematoma, 2022, Neurology India
  • PREFERENCE OF INVESTORS TOWARDS POST OFFICE SAVING SCHEMES, 2024, International Journal of Advanced Research

Their main fields of study include Medicine and Business, Management, and Accounting, with Medicine being the more frequent focus. Specific subfields they have contributed to are Neurology, Accounting, Surgery, and Economics and Econometrics.

Research topics commonly addressed by Rakesh Agrawal comprise:

  • Intracerebral and Subarachnoid Hemorrhage Research
  • Neurosurgical Procedures and Complications
  • Spinal Hematomas and Complications
  • Financial Literacy, Pension, Retirement Analysis
  • Islamic Finance and Banking Studies
  • Housing Market and Economics

Rakesh Agrawal has collaborated with several researchers on their projects. Frequent co-authors include Shivang Dwivedi, Dinesh Gupta, Arun Mishra, Shubhangi Vitthal Gaikwad, and Vandana Kushwaha.

Their works have appeared in a variety of academic publication venues, including:

  • International Journal of Religion
  • Neurology India
  • International Journal of Advanced Research

An award of note received by Rakesh Agrawal is the Fellow of the Indian National Academy of Engineering (INAE), signifying recognition within the engineering and scientific community.

Best Publications

  • Mining association rules between sets of items in large databases

    Rakesh Agrawal;Tomasz Imieliński;Arun Swami

  • Fast algorithms for mining association rules

    Rakesh Agrawal;Ramakrishnan Srikant

  • Fast Algorithms for Mining Association Rules in Large Databases

    Rakesh Agrawal;Ramakrishnan Srikant

  • Mining sequential patterns

    R. Agrawal;R. Srikant

  • Privacy-preserving data mining

    Rakesh Agrawal;Ramakrishnan Srikant

  • Automatic subspace clustering of high dimensional data for data mining applications

    Rakesh Agrawal;Johannes Gehrke;Dimitrios Gunopulos;Prabhakar Raghavan

  • Mining Sequential Patterns: Generalizations and Performance Improvements

    Ramakrishnan Srikant;Ramakrishnan Srikant;Rakesh Agrawal

  • Fast discovery of association rules

    Rakesh Agrawal;Heikki Mannila;Ramakrishnan Srikant;Hannu Toivonen

  • Efficient Similarity Search In Sequence Databases

    Rakesh Agrawal;Christos Faloutsos;Arun N. Swami

  • Mining quantitative association rules in large relational tables

    Ramakrishnan Srikant;Rakesh Agrawal

  • Mining Generalized Association Rules

    Ramakrishnan Srikant;Rakesh Agrawal

  • Database mining: a performance perspective

    R. Agrawal;T. Imielinski;A. Swami

  • Parallel mining of association rules

    R. Agrawal;J.C. Shafer

  • Order preserving encryption for numeric data

    Rakesh Agrawal;Jerry Kiernan;Ramakrishnan Srikant;Yirong Xu

  • Data privacy through optimal k-anonymization

    R.J. Bayardo;Rakesh Agrawal

  • SPRINT: A Scalable Parallel Classifier for Data Mining

    John C. Shafer;Rakesh Agrawal;Manish Mehta

  • Mining association rules with item constraints

    Ramakrishnan Srikant;Quoc Vu;Rakesh Agrawal

  • Fast algorithm for mining association rules

    R. Agrawal

  • Diversifying search results

    Rakesh Agrawal;Sreenivas Gollapudi;Alan Halverson;Samuel Ieong

  • SLIQ: A Fast Scalable Classifier for Data Mining

    Manish Mehta;Rakesh Agrawal;Jorma Rissanen

  • Mining generalized association rules

    Ramakrishnan Srikant;Rakesh Agrawal

Frequent Co-Authors

Hugh W. Hillhouse
Hugh W. Hillhouse University of Washington
Fabio H. Ribeiro
Fabio H. Ribeiro Purdue University West Lafayette
Eric A. Stach
Eric A. Stach University of Pennsylvania
W. Nicholas Delgass
W. Nicholas Delgass Purdue University West Lafayette
Mark Lundstrom
Mark Lundstrom Purdue University West Lafayette
Hilkka I. Kenttämaa
Hilkka I. Kenttämaa Purdue University West Lafayette
Jeffrey T. Miller
Jeffrey T. Miller Purdue University West Lafayette
Thomas Unold
Thomas Unold Helmholtz-Zentrum Berlin für Materialien und Energie
Mahdi M. Abu-Omar
Mahdi M. Abu-Omar University of California, Santa Barbara
Muhammad A. Alam
Muhammad A. Alam Purdue University West Lafayette

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