In this blog post, I will provide a brief report about the 12th Intern. Conference on Machine Learning and Data Mining (MLDM 2016), that I have attended from the 18th to 20th July 2016 in Newark, USA.
About the location (not in New York… but in Newark)
Note that even if it is said that the conference is in New York on the proceedings book cover and some other place on the website of the conference. But the conference was actually held in Newark, which is about 45 min from New York by train. This is not the best location. The conference was held in a hotel close to the Newark airport, surrounded by highways. Thus, it was not possible to walk anywhere after the conference. The only way to get to New York from there is to take a shuttle to go back to the airport and then a train to New York, which takes about 1 hour. I understand that the choice of this location might have been to reduce the cost for the conference. However, the registration for this conference is already higher than many conferences published by Springer. In my opinion, it would have been better if the conference was held in New York.
About the conference
This is the 12th edition of the conference. The MLDM conference is co-located and co-organized with the 16th Industrial Conference on Data Mining 2016, that I have also attended this week. The proceedings of MLDM are published by Springer. Moreover, an extra book was offered containing two late papers, published by Ibai solutions.
The acceptance rate of the conference is about 33% (58 papers have been accepted from 169 submitted papers), which is reasonable.
First day of the conference
The first day of the MLDM conference started at 9:00 with an opening ceremony, followed by a keynote on supervised clustering. The idea of supervised clustering is to perform clustering on data that has already some class labels. Thus, it can be used for example to discover sub-class in existing classes. The class labels can also be used to evaluate how good some clusters are. One of the cluster evaluation measure suggested by the keynote speaker is the purity, that is the percentage of instances having the most popular class label in a cluster. The purity measure can be used to remove outliers from some clusters among other applications.
After the keynote, there was paper presentations for the rest of the day. Topics were quite varied. It included paper presentations about clustering, support vector machines, stock market prediction, list price optimization, image processing, automatic authorship attribution of texts, driving style identification, and source code mining.
The conference ended at around 17:00 and was followed by a banquet at 18:00. There was about 40 persons attending the conference in the morning. Overall, there was some some interesting paper presentations and discussion.
Second day of the conference
The second day was also a day of paper presentations.
The topics of the second day included itemset mining algorithms, inferring geo-information about persons, multigroup regression, analyzing the content of videos, time-series classification, gesture recognition (a presentation by Intel) and analyzing the evolution of communities in social networks.
I have presented two papers during that day (one by me and one by my colleague), including a paper about high-utility itemset mining.
Third day of the conference
The third day of the conference was also paper presentations. There was various topics such as image classification, image enhancement, mining patterns in cellular radio access network data, random forest learning, clustering and graph mining.
It was globally an interesting conference. I have attended both the Industrial Conference on Data Mining and MLDM conference this week. The MLDM is more focused on theory and the Industrial Conference on Data Mining conference is more focused on industrial applications. MLDM is a slightly bigger conference.
Both of these conferences are not very big, but are at least published by Springer, which ensures some visibility to the published papers.
Philippe Fournier-Viger is a full professor and the founder of the open-source data mining software SPMF, offering more than 110 data mining algorithms. If you like this blog, you can tweet about it and/or subscribe to my twitter account @philfv to get notified about new posts.