His primary areas of investigation include Theoretical computer science, Data mining, Graph, Vertex and Reachability. His work deals with themes such as Metric, Core, Set, Maximal set and Range, which intersect with Theoretical computer science. His work on ID3 algorithm, Fractal tree index, Decision tree learning and FSA-Red Algorithm as part of general Data mining study is frequently linked to Hoeffding's inequality, bridging the gap between disciplines.
His Graph study combines topics in areas such as Transitive closure, Set cover problem, Directed acyclic graph and Bounded function. His Vertex research is multidisciplinary, incorporating perspectives in Link analysis, Dense graph, Bipartite graph and Vertex. His Transitive reduction study integrates concerns from other disciplines, such as Graph database, Minimum degree spanning tree, Spanning tree and Directed graph.
His main research concerns Data mining, Theoretical computer science, Graph, Artificial intelligence and Parallel computing. Ruoming Jin has included themes like Cluster analysis, Graph, Set and Automatic summarization in his Data mining study. His Theoretical computer science study combines topics from a wide range of disciplines, such as Metric, Mathematical optimization, Approximation algorithm, Scale and Robustness.
His Vertex study in the realm of Graph interacts with subjects such as Shortest distance. His research in Artificial intelligence tackles topics such as Machine learning which are related to areas like Social network. The various areas that he examines in his Parallel computing study include Scalability and Distributed shared memory.
Ruoming Jin mostly deals with Theoretical computer science, Artificial intelligence, Robustness, Adversarial system and Differential privacy. The concepts of his Theoretical computer science study are interwoven with issues in Data modeling, Autoencoder, Graph, Pairwise comparison and Graph. Ruoming Jin has researched Graph in several fields, including Multi-label classification and Degree.
His Artificial intelligence study combines topics in areas such as Social media, Machine learning and Metric. While the research belongs to areas of Metric, Ruoming Jin spends his time largely on the problem of Recommender system, intersecting his research to questions surrounding Data mining. His work carried out in the field of Adversarial system brings together such families of science as Scalability and Leverage.
His scientific interests lie mostly in Theoretical computer science, Differential privacy, Robustness, Adversarial system and Noise. His studies in Theoretical computer science integrate themes in fields like Autoencoder, Matching, Consistency, Pairwise comparison and Homophily. His Autoencoder research integrates issues from Data modeling, Feature learning, Generative adversarial network and Graph.
Ruoming Jin has researched Differential privacy in several fields, including Property, Deep learning, Artificial intelligence and Scale. His Robustness study frequently intersects with other fields, such as Gaussian noise. His research integrates issues of Scalability, Distributed computing and Leverage in his study of Adversarial system.
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Efficient decision tree construction on streaming data
Ruoming Jin;Gagan Agrawal.
knowledge discovery and data mining (2003)
3-HOP: a high-compression indexing scheme for reachability query
Ruoming Jin;Yang Xiang;Ning Ruan;David Fuhry.
international conference on management of data (2009)
Shared Memory Paraellization of Data Mining Algorithms: Techniques, Programming Interface, and Performance.
Ruoming Jin;Gagan Agrawal.
siam international conference on data mining (2002)
A Survey of Algorithms for Dense Subgraph Discovery
Victor E. Lee;Ning Ruan;Ruoming Jin;Charu C. Aggarwal.
Managing and Mining Graph Data (2010)
Efficiently answering reachability queries on very large directed graphs
Ruoming Jin;Yang Xiang;Ning Ruan;Haixun Wang.
international conference on management of data (2008)
Shared memory parallelization of data mining algorithms: techniques, programming interface, and performance
Ruoming Jin;Ge Yang;G. Agrawal.
IEEE Transactions on Knowledge and Data Engineering (2005)
A Topic Modeling Approach and Its Integration into the Random Walk Framework for Academic Search
Jie Tang;Ruoming Jin;Jing Zhang.
international conference on data mining (2008)
Distance-constraint reachability computation in uncertain graphs
Ruoming Jin;Lin Liu;Bolin Ding;Haixun Wang.
very large data bases (2011)
An algorithm for in-core frequent itemset mining on streaming data
Ruoming Jin;G. Agrawal.
international conference on data mining (2005)
Fast and exact out-of-core and distributed k -means clustering
Ruoming Jin;Anjan Goswami;Gagan Agrawal.
Knowledge and Information Systems (2006)
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