I recently came back from the Machine Learning and Data Mining (MLDM) conference in Leipzig, Germany. This was an interesting meeting with various subjects. Out of the usual subjects such as classification (SVM, etc.), feature selection and clustering, a lot of papers were dedicated to applications of data mining.
Examples of application domains are:
- Intrusion detection
- Marketing data
- Image mining
- Medical and biological data mining
- Text and document mining
- Spam, Newsgroup, blog
A unique session was organized during three days. In comparison to huge conferences with parallel sessions, here the advantage is that more people are attending your presentation. I was personally there to present my work on cluster validity. The most interesting presentation, in my opinion, was the invited talk given by Anil K. Jain about data clustering (certainly because I’m myself involved in clustering). In the next post, I will point out some of his conclusions and recommendations for clustering.