His scientific interests lie mostly in Theoretical computer science, Data mining, Graph, Information retrieval and Artificial intelligence. His studies deal with areas such as Graph, Graph partition, Null graph and Graph factorization as well as Theoretical computer science. Haixun Wang works on Data mining which deals in particular with Data stream mining.
Haixun Wang focuses mostly in the field of Graph, narrowing it down to topics relating to Reachability and, in certain cases, Block graph, Bipartite graph, Transitive closure, Directed acyclic graph and Directed graph. His work on RDF, Topic model and Search engine indexing as part of his general Information retrieval study is frequently connected to Noisy text analytics, thereby bridging the divide between different branches of science. His work deals with themes such as Machine learning, Pattern recognition and Natural language processing, which intersect with Artificial intelligence.
His primary areas of investigation include Data mining, Artificial intelligence, Information retrieval, Theoretical computer science and Data stream mining. His studies in Data mining integrate themes in fields like Data set, Set, Search engine indexing and Cluster analysis. His Artificial intelligence research incorporates themes from Machine learning, Pattern recognition and Natural language processing.
His Information retrieval study frequently links to other fields, such as Web page. His Theoretical computer science study integrates concerns from other disciplines, such as Algorithm design and Graph database, Graph. The study incorporates disciplines such as Data stream, SQL, Knowledge extraction and Concept mining in addition to Data stream mining.
Haixun Wang spends much of his time researching Artificial intelligence, Natural language processing, Semantics, Natural language and Information retrieval. Haixun Wang has researched Artificial intelligence in several fields, including Text mining and Machine learning. His studies deal with areas such as Question answering, Database and Knowledge base as well as Natural language.
His Information retrieval research is multidisciplinary, incorporating perspectives in Data science, Knowledge extraction, Cluster analysis and Knowledge engineering. His Semantic change research includes elements of Relationship extraction and Data mining. His study in Data mining is interdisciplinary in nature, drawing from both Theoretical computer science and Data set.
Haixun Wang mainly focuses on Artificial intelligence, Natural language processing, Knowledge base, Information retrieval and Semantics. His study looks at the relationship between Artificial intelligence and topics such as Machine learning, which overlap with Identification. The concepts of his Natural language processing study are interwoven with issues in Domain, Semantic computing and Conceptualization.
His Knowledge base research incorporates elements of RDF, Graph based, Question answering, RDF query language and Natural language. In his study, Cluster analysis, Text retrieval, Deep learning and Hash function is strongly linked to Text processing, which falls under the umbrella field of Information retrieval. His Graph research is multidisciplinary, relying on both Subgraph isomorphism problem, Graph and Theoretical computer science.
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Mining concept-drifting data streams using ensemble classifiers
Haixun Wang;Wei Fan;Philip S. Yu;Jiawei Han.
knowledge discovery and data mining (2003)
Probase: a probabilistic taxonomy for text understanding
Wentao Wu;Hongsong Li;Haixun Wang;Kenny Q. Zhu.
international conference on management of data (2012)
Managing and Mining Graph Data
Charu C. Aggarwal;Haixun Wang.
BLINKS: ranked keyword searches on graphs
Hao He;Haixun Wang;Jun Yang;Philip S. Yu.
international conference on management of data (2007)
Clustering by pattern similarity in large data sets
Haixun Wang;Wei Wang;Jiong Yang;Philip S. Yu.
international conference on management of data (2002)
Trinity: a distributed graph engine on a memory cloud
Bin Shao;Haixun Wang;Yatao Li.
international conference on management of data (2013)
ViST: a dynamic index method for querying XML data by tree structures
Haixun Wang;Sanghyun Park;Wei Fan;Philip S. Yu.
international conference on management of data (2003)
Landmarks: a new model for similarity-based pattern querying in time series databases
C.-S. Perng;H. Wang;S.R. Zhang;D.S. Parker.
international conference on data engineering (2000)
/spl delta/-clusters: capturing subspace correlation in a large data set
Jiong Yang;Wei Wang;Haixun Wang;P. Yu.
international conference on data engineering (2002)
Moment: maintaining closed frequent itemsets over a stream sliding window
Yun Chi;Haixun Wang;P.S. Yu;R.R. Muntz.
international conference on data mining (2004)
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