Basic Information

  • Paper Review
    All submissions to the ICDMML will be sent to at least 2 reviewers and evaluated based on originality, technical and research content, relevance to conference, contributions, and readability. The full paper submissions will be chosen based on technical merit, interest, applicability, and how well they fit a coherent and balanced technical program.
  • Important Dates
  • Registration Deadline: March 29th 2019
  • Conference Date: April 29-30 2019
  • Publishing
    The accepted papers will be published by ACM International Conference Proceedings Series. The ISBN number assigned to ICDMML 2019 is 978-1-4503-6090-6.
  • Indexes
    Papers will submit the proceedings to Ei Compendex, Scopus for indexing.
  • Note
    Publisher sends all of its published materials to the indexing services. But please note that DBLP, Scopus, Engineering Village, EI Compendex, CPCI(Web of Science), etc., are all entirely separate entities from Publisher. As such, we cannot guarantee that a certain proceedings volume content will pass their review process – or, even if it does pass the review process, when exactly that volume will be indexed. They all have their own guidelines for acceptance, independent of Publisher's influence.
Dates Submission Registration

Data Mining + Machine Learning 2019 Themes

Call for Papers

Artificial Intelligence

Artificial intelligence is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents".

Data Mining

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] It is an essential process where intelligent methods are applied to extract data patterns.

Machine Learning

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

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