CFP: ACM Transactions on Knowledge Discovery from Data
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. Subjects include scalable and effective algorithms for data mining and big data analysis, mining brain networks, mining data streams and more.
ACM Transactions on Knowledge Discovery from Data
Editor-in-Chief: Philip S. Yu, University of Illinois at Chicago, USA
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, but not limited to: scalable and effective algorithms for data mining and big data analysis, mining brain networks, 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 including massively parallel processing and cloud computing platforms.
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.
TKDD welcomes papers that both lay theoretical foundations for data mining, big data and those that provide new insights into the design and implementation of large-scale data mining systems and tools, data mining interface tools, and data mining tools that integrate with the overall information processing infrastructure. The emphasis on integration of theory and practice is an attempt to encourage authors of theory papers to consider applicability and/or implementability of the theoretical results, while encouraging authors of systems papers to reflect on the theoretical results that may have been used in building the systems and/or to offer suggestions on issues that may require theoretical treatment.
For further information and to submit you manuscript, please visit tkdd.acm.org
Charu Aggarwal, IBM T. J. Watson Research, USA
Diane J. Cook, Washington State University, USA
Ian Davidson, University of California at Davis, USA
Wei Fan, Huawei Noah’s Ark Lab, Hong Kong
Johannes Gehrke, Cornell University, USA
Aristides Gionis, Aalto University, Finland
Rong Jin, Michigan State University, USA
George Karypis, University of Minnesota, USA
Irwin King, The Chinese University of Hong Kong, Hong Kong
Ravi Kumar, Google, USA
Tao Li, Florida International University, USA
Srinivasan Parthasarathy, The Ohio State University, USA
Jian Pei, Simon Fraser University, Canada
Jimeng Sun, Georgia Institute of Technology, USA
Vincent S. Tseng, National Cheng Kung University, Taiwan
Ke Wang, Simon Fraser University, Canada
Xindong Wu, University of Vermont, USA
Mohammed J. Zaki, Rensselaer Polytechnic Institute, USA