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The ACM Transactions on Knowledge Discovery from Data (TKDD) publishes original archival papers in the area of knowledge discovery from data and closely related disciplines. The majority of the papers that appear in TKDD is expected to address the logical and technical foundation of knowledge discovery and data mining.
The international Editorial Board is composed of recognized experts in the various subareas of this field, all with a commitment to maintain TKDD as the premier publication in this active field. Papers should be submitted electronically to ACM TKDD manuscript center. The Editorial Board maintains contact with ACM's Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), as well as with other societies, to encourage submittal of advanced and original papers. When appropriate, concise results may be submitted as technical notes; technical comments on earlier publications are welcome as well.
The journal appears in the ACM Digital Library and is thus available to the many individual and institutional DL subscribers. TKDD will be also included in the SIGKDD Anthology and SIGKDD Digital Symposium Collection CDROM publications. These disparate media (print, web, CDROM, DVDROM), widely distributed, ensure that TKDD articles are easily available to knowledge discovery and data mining researchers.
The existence of TKDD has helped to define the field of knowledge discovery and data mining research. It encompasses the development, formalization, and validation of abstractions and models to describe data mining applications and the design and implementation methods for knowledge discovery and automated analysis of large amount of data.