Aim & Scope
The ACM Transactions on Knowledge Discovery from Data (TKDD) welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include: scalable and effective algorithms for data mining and data warehousing, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology. [1]
Series / Collection
2691-1922 ( Print )
2577-3224 ( Online )
Association for Computing Machinery (ACM)
,
Institute of Mathematical Statistics (IMS)
1936-7228 ( Print )
1936-7236 ( Online )
Association for Computing Machinery (ACM)
1549-6325 ( Print )
1549-6333 ( Online )
Association for Computing Machinery (ACM)
1544-3558 ( Print )
1544-3965 ( Online )
Association for Computing Machinery (ACM)
1544-3566 ( Print )
1544-3973 ( Online )
Association for Computing Machinery (ACM)
2375-4699 ( Print )
2375-4702 ( Online )
Association for Computing Machinery (ACM)
1530-0226 ( Print )
1558-3430 ( Online )
Association for Computing Machinery (ACM)
1556-4665 ( Print )
1556-4703 ( Online )
Association for Computing Machinery (ACM)
1942-3454 ( Print )
1942-3462 ( Online )
Association for Computing Machinery (ACM)
1529-3785 ( Print )
1557-945X ( Online )
Association for Computing Machinery (ACM)
2024
Quantum Nearest Neighbor Collaborative Filtering Algorithm for Recommendation System
J Li , J Shi , J Zhang , ... , S Zhang
ACM Transactions on Knowledge Discovery from Data (TKDD) , 2024
Towards Faster Deep Graph Clustering via Efficient Graph Auto-Encoder
S Ding , B Wu , L Ding , ... , X Wu
ACM Transactions on Knowledge Discovery from Data (TKDD) , 2024
H Zhang , Z Liu , C Shang , ... , Y Jiang
ACM Transactions on Knowledge Discovery from Data (TKDD) , 2024
Subspace-Contrastive Multi-View Clustering
L Fu , S Huang , L Zhang , ... , C Chen
ACM Transactions on Knowledge Discovery from Data (TKDD) , 2024
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
Y Liu , M Li , X Li , ... , R Guan
ACM Transactions on Knowledge Discovery from Data (TKDD) , 2024
Spatio-Temporal Parallel Transformer based model for Traffic Prediction
R Kumar , J Mendes-Moreira , J Chandra
ACM Transactions on Knowledge Discovery from Data (TKDD) , 2024
A Survey on Knowledge Graph Related Research in Smart City Domain
ACM Transactions on Knowledge Discovery from Data (TKDD) , 2024
Concept Evolution Detecting over Feature Streams
P Zhou , Y Guo , H Yu , ... , X Wu
ACM Transactions on Knowledge Discovery from Data (TKDD) , 2024
Fair Federated Learning with Multi-Objective Hyperparameter Optimization
ACM Transactions on Knowledge Discovery from Data (TKDD) , 2024
B Peng , Z Chen , S Parthasarathy , X Ning
ACM Transactions on Knowledge Discovery from Data (TKDD) , 2024
Editorial Retractions, Expressions of Concern and External Notices
Multisource domain adaptation and its application to early detection of fatigue
R Chattopadhyay , Q Sun , W Fan , ... , J Ye
ACM Transactions on Knowledge Discovery from Data (TKDD)2012 - VOLUME 6, ISSUE 4 pp 1-26.
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