In this blog post, I will talk about the ECML PKDD 2020 conference, that was held from the 14th to 18th September 2020. This post will be a little bit brief because I did not attend the whole conference but just a few presentations.
What is PKDD?
The PKDD conference is the number one data mining and machine learning conference in europe. This year, it was the 31st edition of this conference. The PKDD conference proceedings are published by Springer in the Lecture Notes in Computer Sciences (LNCS) series, which gives good visibility to the papers. Moreover, it is noteworthy that workshop papers are also published in Springer LNCS volumes.
Due to the coronavirus pandemic, the conference was held online but was supposed to be held in Ghent Belgium.
Many of the papers and videos have been made available online on the website of the conference: https://slideslive.com/ecmlpkdd2020/main-track-research-track
I have been watching a few of them, and it has been very interesting, as papers of this conference are high quality papers.
The PKDD 2020 conference has 5 keynotes, an applied data science track, a research paper track, industry track, demo track, workshops, tutorials, and a journal paper track.
I will here report important information that was presented during the PKDD 2020 opening ceremony.
There was about 1000 persons involved in the program committee for reviewing papers, and about 1000 attendees. It was explained that hundreds of people were recruited this year to join the program committee due to the increase of papers in machine learning.
In the research track, this year 687 papers were submitted. From that, 131 were accepted. Thus, the acceptance rate was 19.1 %. Here is a few slides about the research track:
For the Applied Data Science track, 235 papers were submitted, and 65 accepted. Thus, the acceptance rate was 28 %, which is quite higher than the research track. Here is the number of papers by topic:
For the demo track, 23 papers were submitted, and 10 were accepted, for an acceptance rate of 43 %. Some information about this track:
Here are the statistics about the papers submitted to the DMKD or Machine learning journals for the journal track :
For the industry track, the acceptance rate was about 50%:
This is about the diversity of authors in terms of regions:
Here are the best data mining papers:
And this was the best applied data science paper:
Pattern mining papers
As I am interested by the topic of pattern mining, I have made a list of the main papers on this topic published in the PKDD 2020 conference:
- Maximum Margin Separations in Finite Closure Systems
Florian Seiffarth (University of Bonn); Tamas Horvath (University Bonn); Stefan Wrobel (Fraunhofer IAIS & Univ. of Bonn)
- Discovering outstanding subgroup lists for numeric targets using MDL
Hugo Manuel Proença (LIACS); Peter Grünwald (CWI); Thomas Bäck (LIACS); Matthijs van Leeuwen (LIACS)
- A Relaxation-based Approach for Mining Diverse Closed Patterns
Arnold Hien et al.
- OMBA: User-Guided Product Representations for Online Market Basket Analysis
Amila Silva (The University of Melbourne); Ling Luo (The University of Melbourne); Shanika Karunasekera (The University of Melbourne); Christopher Leckie (The University of Melbourne)
That is all for my report about the PKDD 2020 conference. The report is not very long because of my busy schedule. Hence, I only watched a few presentations from the conference. Hope this report has still been interesting.
Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 170data mining algorithms.