His primary areas of study are Data mining, Database, Information retrieval, Data structure and Object. His work on Association rule learning and Table as part of general Data mining study is frequently linked to Information sensitivity, bridging the gap between disciplines. His work on Query optimization and Query language as part of his general Database study is frequently connected to Clickstream, thereby bridging the divide between different branches of science.
His work deals with themes such as Distributed database, Query by Example, Spatial query, Query expansion and View, which intersect with Query optimization. In the field of Information retrieval, his study on Recommender system and Search engine indexing overlaps with subjects such as Polyphony and MIDI. His research investigates the connection between Data structure and topics such as String searching algorithm that intersect with problems in Approximate string matching.
His scientific interests lie mostly in Data mining, Information retrieval, Database, Query language and Set. He has researched Data mining in several fields, including Data stream and Probabilistic logic. His Information retrieval study incorporates themes from Object, User interface, Data modeling and Index.
Arbee L. P. Chen combines subjects such as Distributed database, RDF query language, Query optimization, Schema and View with his study of Query language. His Distributed database study combines topics in areas such as Relational database and Theoretical computer science. He has included themes like Algorithm and Tuple in his Set study.
Arbee L. P. Chen focuses on Data mining, Skyline, Set, Artificial intelligence and Information retrieval. His Data mining study combines topics from a wide range of disciplines, such as Line segment, Series and Similarity. His Skyline research also works with subjects such as
The various areas that Arbee L. P. Chen examines in his Set study include Computational complexity theory, Theoretical computer science and Mathematical optimization, Greedy algorithm. His Artificial intelligence research incorporates elements of Social influence, Social media, Machine learning and Depression. His Information retrieval research includes themes of Graph database, String and Mobile device.
Arbee L. P. Chen mainly focuses on Artificial intelligence, Data mining, Mobile device, Probabilistic logic and Skyline. His Artificial intelligence research is multidisciplinary, relying on both Cognitive psychology, Social media, Machine learning and Depression. His studies in Machine learning integrate themes in fields like Scalability, Human behavior and Information retrieval.
As part of his studies on Data mining, Arbee L. P. Chen often connects relevant areas like Parallel processing. His Probabilistic logic research is multidisciplinary, incorporating perspectives in Graph, Apriori algorithm and Series. His Skyline research is multidisciplinary, incorporating elements of Process and Filter.
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A music recommendation system based on music data grouping and user interests
Hung-Chen Chen;Arbee L. P. Chen.
conference on information and knowledge management (2001)
Mermaid—A front-end to distributed heterogeneous databases
M. Templeton;D. Brill;S.K. Dao;E. Lund.
Proceedings of the IEEE (1987)
Hiding Sensitive Association Rules with Limited Side Effects
Yi-Hung Wu;Chia-Ming Chiang;A.L.P. Chen.
IEEE Transactions on Knowledge and Data Engineering (2007)
Mining Frequent Itemsets from Data Streams with a Time-Sensitive Sliding Window
Chih-Hsiang Lin;Ding-Ying Chiu;Yi-Hung Wu;Arbee L. P. Chen.
siam international conference on data mining (2005)
Discovering nontrivial repeating patterns in music data
Jia-Lien Hsu;Chih-Chin Liu;A.L.P. Chen.
IEEE Transactions on Multimedia (2001)
Efficient repeating pattern finding in music databases
Jia-Lien Hsu;Arbee L. P. Chen;C.-C. Liu.
conference on information and knowledge management (1998)
A graph-based approach for discovering various types of association rules
Show-Jane Yen;A.L.P. Chen.
IEEE Transactions on Knowledge and Data Engineering (2001)
An efficient approach to discovering knowledge from large databases
Show-Jane Yen;A.L.P. Chen.
international conference on parallel and distributed information systems (1996)
Optimal index and data allocation in multiple broadcast channels
Shou-Chih Lo;A.L.P. Chen.
international conference on data engineering (2000)
Enabling personalized recommendation on the Web based on user interests and behaviors
Yi-Hung Wu;Yong-Chuan Chen;A.L.P. Chen.
international workshop on research issues in data engineering (2001)
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