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
Computer Science D-index 48 Citations 24,252 150 World Ranking 3173 National Ranking 1661

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Machine learning
  • Artificial intelligence

Arindam Banerjee mainly investigates Cluster analysis, Artificial intelligence, Data mining, Pattern recognition and Bregman divergence. The various areas that Arindam Banerjee examines in his Artificial intelligence study include Covariance matrix, Machine learning, Divergence and Riemannian manifold. His study looks at the relationship between Machine learning and topics such as Topic model, which overlap with Variety, Structure and Inference.

His research integrates issues of Matrix decomposition, Probabilistic logic and Prior probability in his study of Data mining. His biological study spans a wide range of topics, including Semi supervised clustering and Longest common subsequence problem. His Bregman divergence research includes elements of Gradient descent and Linear programming, Algorithm, Mathematical optimization, Squared euclidean distance.

His most cited work include:

  • Anomaly detection: A survey (6140 citations)
  • Clustering with Bregman Divergences (1298 citations)
  • Semi-supervised Clustering by Seeding (731 citations)

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

Arindam Banerjee spends much of his time researching Artificial intelligence, Algorithm, Machine learning, Data mining and Mathematical optimization. He combines topics linked to Pattern recognition with his work on Artificial intelligence. His studies deal with areas such as Structure and Matrix as well as Algorithm.

Arindam Banerjee has included themes like Multi-task learning, Latent Dirichlet allocation and Covariance in his Machine learning study. His Anomaly detection study, which is part of a larger body of work in Data mining, is frequently linked to Aviation safety, bridging the gap between disciplines. His work deals with themes such as Scalability, Estimator, Convex function and Bregman divergence, Applied mathematics, which intersect with Mathematical optimization.

He most often published in these fields:

  • Artificial intelligence (31.02%)
  • Algorithm (21.63%)
  • Machine learning (19.18%)

What were the highlights of his more recent work (between 2017-2021)?

  • Algorithm (21.63%)
  • Artificial intelligence (31.02%)
  • Applied mathematics (11.84%)

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

Arindam Banerjee mainly investigates Algorithm, Artificial intelligence, Applied mathematics, Generalization and Natural language processing. His Algorithm research incorporates elements of Mixture model, Parameter identification problem, Autoencoder and Density estimation. His Artificial intelligence study frequently links to adjacent areas such as Machine learning.

His Machine learning research is multidisciplinary, relying on both Multi-task learning, El Niño and Disease. His work carried out in the field of Applied mathematics brings together such families of science as Random matrix, Quadratic form, Isometry, Linear model and Gaussian noise. His Natural language processing research integrates issues from Graphical model, Structure and Inference.

Between 2017 and 2021, his most popular works were:

  • TRY plant trait database : Enhanced coverage and open access (179 citations)
  • Intelligent systems for geosciences: an essential research agenda (28 citations)
  • Adversarial attacks on an oblivious recommender (21 citations)

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

  • Statistics
  • Machine learning
  • Artificial intelligence

Applied mathematics, Generalization, Adversarial system, Computer security and Recommender system are his primary areas of study. His Applied mathematics study which covers MNIST database that intersects with Synthetic data. Throughout his Generalization studies, he incorporates elements of other sciences such as Uniform convergence, Connection, Distribution, Deep learning and Space.

The study incorporates disciplines such as Adversary, Generator, Variety and Vulnerability in addition to Adversarial system. Arindam Banerjee undertakes interdisciplinary study in the fields of Adversary and Context through his works. Arindam Banerjee conducted interdisciplinary study in his works that combined Computer security and Focus.

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

Anomaly detection: A survey

Varun Chandola;Arindam Banerjee;Vipin Kumar.
ACM Computing Surveys (2009)

8386 Citations

Clustering with Bregman divergences

Arindam Banerjee;Srujana Merugu;Inderjit S. Dhillon;Joydeep Ghosh.
siam international conference on data mining (2004)

1677 Citations

Semi-supervised Clustering by Seeding

Sugato Basu;Arindam Banerjee;Raymond J. Mooney.
international conference on machine learning (2002)

1158 Citations

Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

Arindam Banerjee;Inderjit S. Dhillon;Joydeep Ghosh;Suvrit Sra.
Journal of Machine Learning Research (2005)

827 Citations

Active Semi-Supervision for Pairwise Constrained Clustering

Sugato Basu;Arindam Banerjee;Raymond J. Mooney.
siam international conference on data mining (2004)

661 Citations

A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation

Arindam Banerjee;Inderjit Dhillon;Joydeep Ghosh;Srujana Merugu.
Journal of Machine Learning Research (2007)

506 Citations

Anomaly Detection for Discrete Sequences: A Survey

V. Chandola;A. Banerjee;V. Kumar.
IEEE Transactions on Knowledge and Data Engineering (2012)

485 Citations

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

Anuj Karpatne;Gowtham Atluri;James H. Faghmous;Michael Steinbach.
IEEE Transactions on Knowledge and Data Engineering (2017)

357 Citations

Clickstream clustering using weighted longest common subsequences

A. Banerjee.
Proceedings of the Web Mining Workshop at the 1st SIAM Conference on Data Mining (2001)

313 Citations

Model-based overlapping clustering

Arindam Banerjee;Chase Krumpelman;Joydeep Ghosh;Sugato Basu.
knowledge discovery and data mining (2005)

273 Citations

Best Scientists Citing Arindam Banerjee

Frank Nielsen

Frank Nielsen

Association for Computing Machinery

Publications: 107

Joydeep Ghosh

Joydeep Ghosh

The University of Texas at Austin

Publications: 68

Richard Nock

Richard Nock

Australian National University

Publications: 58

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 45

Jiawei Han

Jiawei Han

University of Illinois at Urbana-Champaign

Publications: 42

Inderjit S. Dhillon

Inderjit S. Dhillon

Amazon (United States)

Publications: 37

Christos Faloutsos

Christos Faloutsos

Carnegie Mellon University

Publications: 36

Vipin Kumar

Vipin Kumar

University of Minnesota

Publications: 34

Jens Kattge

Jens Kattge

Max Planck Society

Publications: 30

Christopher Leckie

Christopher Leckie

University of Melbourne

Publications: 30

Brian J. Enquist

Brian J. Enquist

University of Arizona

Publications: 29

Peter B. Reich

Peter B. Reich

University of Minnesota

Publications: 26

Jing Gao

Jing Gao

Purdue University West Lafayette

Publications: 26

Alfred O. Hero

Alfred O. Hero

University of Michigan–Ann Arbor

Publications: 25

Suvrit Sra

Suvrit Sra

MIT

Publications: 23

Ülo Niinemets

Ülo Niinemets

Estonian University of Life Sciences

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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