difference between IEEE International Conference on Big Data and IEEE ICDM (international conference on data mining)

Question

I'm not sure if this is the right place to ask this question, but I didn't know where to ask.

Recently, I went to the IEEE 2016 International Conference on Big Data in Washington DC, but was surprised to find that there is also a different, IEEE International Conference on Data Mining (ICDM) in Barcelona. Is one more highly ranked than the other, and why does IEEE have two (or more) similar conferences?

Answer 1

They are two different but related topics. You can do Data Mining on Big Data for instance.

If you look at the call for papers to see the tracks of both conference, you will notice they are pretty different.

ICDM 2016:

• Foundations, algorithms, models and theory of data mining
• Machine learning and statistical methods for data mining
• Mining text, semi-structured, spatio-temporal, streaming, graph, web, multimedia data
• Data mining systems and platforms, their efficiency, scalability and privacy
• Data mining in modeling, visualization, personalization and recommendation
• Applications of data mining in all domains including social, web, bioinformatics and finance

BigData2016:

• Big Data Science and Foundations
• Big Data Infrastructure
• Big Data Management
• Big Data Search and Mining
• Big Data Security, Privacy and Trust
• Big Data Applications

Only one track covers both. Big Data Management or infrastructure has nothing to do with Data Mining, and many Data Mining algorithms are not scalable to Big Data.

Maybe your understanding of these concepts led you to think at first that these conferences are similar but they are definitely not. However, I agree that some papers could go in both, but this is the case for many other conferences.

Conference ranking doesn't mean too much when topics are different. If you work with Big Data, your paper will have more impact if published in a conference focused on Big Data because researchers in this field will more easily be reached, and opposite if you work in Data Mining.