Industrial Conference on Data Mining (ICDM) 2021
Event submitted on Monday, October 12th 2020, approved by Henry Dalzel ✓
This event has been tagged as follows:
This event is recommended for all the professionals living and working in New York. Researchers and professionals from different fields will present theoretical aspects and their applications, and the results obtained by applying data mining. It will give the opportunity to meet top leading researchers in Data Mining and Machine Learning from all over the world.
Conference Event Summary
The following description was either submitted by the Conference Organizer on Monday, October 12th 2020, or created by us.
The Industrial Conference on Data Mining ICDM is held on a yearly basis.
Researchers from all over the world will present theoretical and application-oriented topics in Data Mining. Practitioners can present and discuss their ongoing projects in Industry Sessions.
Industrial Exhibition · Best-Paper-Award for Talks and Posters · Workshops: Case-Based Reasoning CBR-MD, DM in Marketing DMM, and B2ML I-Business to Manufacturing and LifeSciences – The Internet of Things and Services.
Authors can submit their paper in long or short version.
The paper must be formatted in the Springer LNCS format. They should have at most 15 pages. The papers will be reviewed by the program committee. Papers will appear in the conference proceedings.
Please submit your Long Paper to the CMS-System.
Short papers are also welcome and can be used to describe work in progress or project ideas. They can have 5 to max. 15 pages, formatted in Springer LNCS format. Accepted short papers will be presented as poster in the poster session. They will be published in a special poster proceedings book.
Please submit your Short Paper and your Industry Paper to the CMS-System.
We encourage industrial people to show their applications and projects for data mining. This work can be presented as poster during the poster session in the special industry track. Please submit a one page abstract including title, name and affilation.