2019 - ACM Distinguished Member
2015 - ACM Senior Member
Vincent S. Tseng spends much of his time researching Data mining, Efficient algorithm, Process, Pruning and Database transaction. Vincent S. Tseng combines subjects such as Data stream, Machine learning, Utility mining and Artificial intelligence with his study of Data mining. His studies examine the connections between Pruning and genetics, as well as such issues in Measure, with regards to Timestamp.
His Database transaction research is multidisciplinary, incorporating perspectives in Tree, Time complexity and Task. His work in Algorithm design addresses subjects such as Knowledge extraction, which are connected to disciplines such as Mobile computing. His work on Scalability as part of general Database research is frequently linked to Tuple, Command-line interface and Implementation, thereby connecting diverse disciplines of science.
His primary areas of investigation include Data mining, Artificial intelligence, Machine learning, Association rule learning and Cluster analysis. His biological study spans a wide range of topics, including Scalability, Fuzzy set, Fuzzy logic and Database transaction, Utility mining. His Scalability research is multidisciplinary, relying on both Video tracking, Wireless sensor network and Big data.
His Artificial intelligence study frequently links to related topics such as Pattern recognition. As part of his studies on Machine learning, Vincent S. Tseng often connects relevant areas like Field. Vincent S. Tseng has researched Association rule learning in several fields, including Algorithm design and Data stream mining.
His primary scientific interests are in Artificial intelligence, Machine learning, Data mining, Deep learning and Data science. His research integrates issues of Medical record and Receiver operating characteristic in his study of Artificial intelligence. His research in Data mining intersects with topics in Scalability, Sliding window protocol, Database transaction, Task and Efficient algorithm.
His study in Scalability is interdisciplinary in nature, drawing from both Sequential Pattern Mining, Measure and Pruning. In his work, Database is strongly intertwined with Speedup, which is a subfield of Database transaction. Vincent S. Tseng has included themes like Time complexity and Key in his Efficient algorithm study.
Artificial intelligence, Machine learning, Database, Data mining and Database transaction are his primary areas of study. His work in Artificial intelligence tackles topics such as Multivariate statistics which are related to areas like Biomedicine and Rendering. Many of his research projects under Database are closely connected to MEDLINE with MEDLINE, tying the diverse disciplines of science together.
His Data mining research is multidisciplinary, incorporating elements of Scalability and Computational intelligence. Vincent S. Tseng has included themes like Range, Sequential Pattern Mining, The Internet and Measure in his Scalability study. In his research on the topic of Database transaction, Time complexity, Task and Key is strongly related with Efficient algorithm.
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Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases
V. S. Tseng;Bai-En Shie;Cheng-Wei Wu;P. S. Yu.
IEEE Transactions on Knowledge and Data Engineering (2013)
UP-Growth: an efficient algorithm for high utility itemset mining
Vincent S. Tseng;Cheng-Wei Wu;Bai-En Shie;Philip S. Yu.
knowledge discovery and data mining (2010)
SPMF: a Java open-source pattern mining library
Philippe Fournier-Viger;Antonio Gomariz;Ted Gueniche;Azadeh Soltani.
Journal of Machine Learning Research (2014)
FHM: Faster High-Utility Itemset Mining using Estimated Utility Co-occurrence Pruning
Philippe Fournier-Viger;Cheng Wei Wu;Souleymane Zida;Vincent Shin-Mu Tseng.
international syposium on methodologies for intelligent systems (2014)
Semantic trajectory mining for location prediction
Josh Jia-Ching Ying;Wang-Chien Lee;Tz-Chiao Weng;Vincent S. Tseng.
advances in geographic information systems (2011)
Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis
Peng Jen Chen;Meng Chiung Lin;Mei Ju Lai;Jung Chun Lin.
Mining user similarity from semantic trajectories
Josh Jia-Ching Ying;Eric Hsueh-Chan Lu;Wang-Chien Lee;Tz-Chiao Weng.
workshop on location-based social networks (2010)
Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns
Ja-Hwung Su;Wei-Jyun Huang;Philip S Yu;Vincent S Tseng.
IEEE Transactions on Knowledge and Data Engineering (2011)
Efficient Algorithms for Mining Top-K High Utility Itemsets
Vincent S. Tseng;Cheng-Wei Wu;Philippe Fournier-Viger;Philip S. Yu.
IEEE Transactions on Knowledge and Data Engineering (2016)
Music Recommendation Using Content and Context Information Mining
Ja-Hwung Su;Hsin-Ho Yeh;P.S. Yu;V.S. Tseng.
IEEE Intelligent Systems (2010)
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