2018 - IEEE Fellow For contributions to graph partitioning and data mining
2014 - ACM Senior Member
George Karypis mostly deals with Data mining, Algorithm, Artificial intelligence, Graph partition and Theoretical computer science. George Karypis has researched Data mining in several fields, including Scalability, Cluster analysis, Document clustering and Graph. His biological study spans a wide range of topics, including Vertex, Graph theory, Key and Collaborative filtering.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Markov model and Pattern recognition. In the field of Machine learning, his study on Recommender system overlaps with subjects such as Space. His research integrates issues of Strength of a graph, Independent set, Graph bandwidth, Sparse matrix and Partition in his study of Graph partition.
His main research concerns Artificial intelligence, Data mining, Machine learning, Algorithm and Parallel computing. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Natural language processing, Sequence and Pattern recognition. His studies deal with areas such as Similarity, Feature selection and Cluster analysis as well as Data mining.
His studies in Recommender system and Collaborative filtering are all subfields of Machine learning research. His Algorithm study incorporates themes from Vertex, Graph partition, Graph, Mathematical optimization and Speedup. George Karypis combines subjects such as Scalability and Sparse matrix with his study of Parallel computing.
His primary areas of study are Artificial intelligence, Machine learning, Graph, Graph and Theoretical computer science. His research in Artificial intelligence intersects with topics in Sequence and Natural language processing. His Recommender system and Unsupervised learning study, which is part of a larger body of work in Machine learning, is frequently linked to Node, bridging the gap between disciplines.
He interconnects Computation and Speedup in the investigation of issues within Graph. His Speedup research is multidisciplinary, incorporating elements of Algorithm, Key and Scale. The concepts of his Theoretical computer science study are interwoven with issues in Scalability, Representation, Mutual information, Cluster analysis and Code.
George Karypis spends much of his time researching Artificial intelligence, Graph, Machine learning, Theoretical computer science and Information retrieval. George Karypis has included themes like Scalability and Graph in his Graph study. His Recommender system and Relevance study in the realm of Machine learning interacts with subjects such as Personalized learning.
He is interested in Collaborative filtering, which is a field of Recommender system. His Theoretical computer science study deals with Computation intersecting with Speedup and Deep learning. His work in the fields of Information retrieval, such as Search engine, intersects with other areas such as Set.
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.
Item-based collaborative filtering recommendation algorithms
Badrul Sarwar;George Karypis;Joseph Konstan;John Riedl.
the web conference (2001)
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
George Karypis;Vipin Kumar.
SIAM Journal on Scientific Computing (1998)
A Comparison of Document Clustering Techniques
Michael Steinbach;George Karypis;Vipin Kumar.
(2000)
Chameleon: hierarchical clustering using dynamic modeling
G. Karypis;Eui-Hong Han;V. Kumar.
IEEE Computer (1999)
Analysis of recommendation algorithms for e-commerce
Badrul Sarwar;George Karypis;Joseph Konstan;John Riedl.
electronic commerce (2000)
Item-based top- N recommendation algorithms
Mukund Deshpande;George Karypis.
ACM Transactions on Information Systems (2004)
Introduction to parallel computing: design and analysis of algorithms
Vipin Kumar;Ananth Grama;Anshul Gupta;George Karypis.
(1994)
Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus
Emily C. Baechler;Franak M. Batliwalla;George Karypis;Patrick M. Gaffney.
Proceedings of the National Academy of Sciences of the United States of America (2003)
Multilevelk-way Partitioning Scheme for Irregular Graphs
George Karypis;Vipin Kumar.
Journal of Parallel and Distributed Computing (1998)
Application of Dimensionality Reduction in Recommender System - A Case Study
Badrul Sarwar;George Karypis;Joseph Konstan;John Riedl.
citeseer.ist.psu.edu/sarwar00application.html (2000)
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Profile was last updated on December 6th, 2021.
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