While SIGKDD has traditionally enjoyed an unusually high quality of reviewing, there is no doubt that publishing in SIGKDD (and other high quality data mining conferences) is very challenging. This is especially true for young faculty, grad students whose primary advisor is not an experienced SIGKDD author, or people from outside the community (i.e. a biologist or mathematician who has a result that might greatly interest the data mining community).

In this tutorial Dr. Keogh will demonstrate some simple ideas to enhance the probability of success in getting your paper published in a top data mining conference; and after the work is published, getting it highly cited.

These tips and tricks are based on 12 years experience as a SIGKDD author and reviewer, and wisdom solicited from many of the most prolific data mining researchers/reviewers.

Topics covered in the tutorial include:

* Finding the right problems to work on (80% of the battle).
* Don't summarize, sell! Writing abstracts that put the reviewer on your side from the start.
* Getting or creating the perfect dataset.
* Experiments that tell a story.
* Making effective and interesting figures.
* Getting the reviewers on your side.
* The top-ten avoidable reasons why papers get rejected from SIGKDD.
* Three simple tricks to increase the number of citations to your work.

While Dr. Keogh does not claim to have a “magic bullet” for publishing in SIGKDD, his significant track record of publishing in top data mining venues, combined with extensive (and deliberately uncredited) experience in helping younger researchers “break-in” to SIGKDD have placed him in a unique position to share useful and actionable advice.

While writing this tutorial Dr. Keogh, sought and received advice from many respected data mining researchers, their advice is incorporated into this tutorial.