D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 109 Citations 154,753 433 World Ranking 17 National Ranking 10

Research.com Recognitions

Awards & Achievements

Fellow of the Indian National Academy of Engineering (INAE)

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Database
  • Operating system

Data mining, Database, Association rule learning, Artificial intelligence and Theoretical computer science are his primary areas of study. His Data mining research includes elements of Classifier and Categorical variable. When carried out as part of a general Database research project, his work on SQL, Relational database management system, Relational database and Database design is frequently linked to work in Hippocratic Oath, therefore connecting diverse disciplines of study.

As a part of the same scientific study, Rakesh Agrawal usually deals with the Association rule learning, concentrating on Database transaction and frequently concerns with Taxonomy and GSP Algorithm. His studies in Artificial intelligence integrate themes in fields like Natural language processing, Machine learning and Pattern recognition. His K-optimal pattern discovery study incorporates themes from Affinity analysis, FSA-Red Algorithm and Molecule mining.

His most cited work include:

  • Mining association rules between sets of items in large databases (13162 citations)
  • Fast algorithms for mining association rules (9841 citations)
  • Fast Algorithms for Mining Association Rules in Large Databases (8859 citations)

What are the main themes of his work throughout his whole career to date?

Rakesh Agrawal mainly investigates Distillation, Analytical chemistry, Data mining, Air separation and Fractionating column. His studies deal with areas such as Oxygen, Liquid oxygen and Nitrogen as well as Analytical chemistry. His Oxygen research is multidisciplinary, relying on both Scientific method and Chemical engineering.

His biological study focuses on Association rule learning. The study incorporates disciplines such as Refrigeration and Argon in addition to Air separation. His Reboiler research is multidisciplinary, incorporating elements of Vacuum distillation and Condenser.

He most often published in these fields:

  • Distillation (17.47%)
  • Analytical chemistry (13.14%)
  • Data mining (10.71%)

What were the highlights of his more recent work (between 2014-2021)?

  • Thin film (5.10%)
  • Nanoparticle (4.97%)
  • Distillation (17.47%)

In recent papers he was focusing on the following fields of study:

Rakesh Agrawal focuses on Thin film, Nanoparticle, Distillation, Chemical engineering and Optoelectronics. His Nanoparticle study is concerned with the field of Nanotechnology as a whole. His Nanotechnology research focuses on Kesterite in particular.

As a member of one scientific family, Rakesh Agrawal mostly works in the field of Distillation, focusing on Process engineering and, on occasion, Waste management, Natural gas, Solar energy, Reboiler and Scientific method. His Chemical engineering study combines topics in areas such as Annealing and Raman spectroscopy. His studies deal with areas such as Characterization, Nanocrystal and Photovoltaic system as well as Optoelectronics.

Between 2014 and 2021, his most popular works were:

  • A synergistic biorefinery based on catalytic conversion of lignin prior to cellulose starting from lignocellulosic biomass (211 citations)
  • 9.0% efficient Cu2ZnSn(S,Se)4 solar cells from selenized nanoparticle inks (185 citations)
  • Improved performance of Ge‐alloyed CZTGeSSe thin‐film solar cells through control of elemental losses (148 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Database
  • Operating system

Rakesh Agrawal mostly deals with Nanoparticle, Nanotechnology, Process engineering, Thin film and Optoelectronics. His Nanoparticle research incorporates themes from Kesterite, Grain growth and Raman spectroscopy. His Nanotechnology study focuses mostly on Nanocrystal and Copper indium gallium selenide solar cells.

The Process engineering study combines topics in areas such as Fuel oil, Waste management, Natural gas, Solar energy and Peaking power plant. His study on Thin film also encompasses disciplines like

  • Chemical engineering together with CZTS,
  • Inorganic chemistry which is related to area like Arsenic sulfide and Catalysis. His work on Energy conversion efficiency and Band gap as part of general Optoelectronics study is frequently linked to Ferroelectricity, therefore connecting diverse disciplines of science.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Fast algorithms for mining association rules

Rakesh Agrawal;Ramakrishnan Srikant.
very large data bases (1998)

26932 Citations

Mining association rules between sets of items in large databases

Rakesh Agrawal;Tomasz Imieliński;Arun Swami.
international conference on management of data (1993)

23413 Citations

Fast Algorithms for Mining Association Rules in Large Databases

Rakesh Agrawal;Ramakrishnan Srikant.
very large data bases (1994)

19416 Citations

Mining sequential patterns

R. Agrawal;R. Srikant.
international conference on data engineering (1995)

8249 Citations

Privacy-preserving data mining

Rakesh Agrawal;Ramakrishnan Srikant.
international conference on management of data (2000)

4221 Citations

Automatic subspace clustering of high dimensional data for data mining applications

Rakesh Agrawal;Johannes Gehrke;Dimitrios Gunopulos;Prabhakar Raghavan.
international conference on management of data (1998)

3827 Citations

Mining Sequential Patterns: Generalizations and Performance Improvements

Ramakrishnan Srikant;Ramakrishnan Srikant;Rakesh Agrawal.
extending database technology (1996)

3680 Citations

Fast discovery of association rules

Rakesh Agrawal;Heikki Mannila;Ramakrishnan Srikant;Hannu Toivonen.
knowledge discovery and data mining (1996)

3650 Citations

Efficient Similarity Search In Sequence Databases

Rakesh Agrawal;Christos Faloutsos;Arun N. Swami.
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms (1993)

2884 Citations

Mining quantitative association rules in large relational tables

Ramakrishnan Srikant;Rakesh Agrawal.
international conference on management of data (1996)

2483 Citations

Best Scientists Citing Rakesh Agrawal

Alejandro Pérez-Rodríguez

Alejandro Pérez-Rodríguez

University of Barcelona

Publications: 52

Fangyang Liu

Fangyang Liu

Central South University

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Jin Hyeok Kim

Jin Hyeok Kim

Chonnam National University

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Victor Izquierdo-Roca

Victor Izquierdo-Roca

Catalonia Energy Research Institute

Publications: 33

Muhammad A. Alam

Muhammad A. Alam

Purdue University West Lafayette

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Richard W. Baker

Richard W. Baker

Saudi Aramco (United States)

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Bert F. Sels

Bert F. Sels

KU Leuven

Publications: 28

Thomas Unold

Thomas Unold

Helmholtz-Zentrum Berlin für Materialien und Energie

Publications: 28

Daocheng Pan

Daocheng Pan

Chinese Academy of Sciences

Publications: 27

David B. Mitzi

David B. Mitzi

Duke University

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Ignacio E. Grossmann

Ignacio E. Grossmann

Carnegie Mellon University

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Oki Gunawan

Oki Gunawan

IBM (United States)

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Byoung Koun Min

Byoung Koun Min

Korea Institute of Science and Technology

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Lydia Helena Wong

Lydia Helena Wong

Nanyang Technological University

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José A. Caballero

José A. Caballero

University of Alicante

Publications: 23

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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