IEEE ICDM Contest – Overview of Top Solutions, part 1

The IEEE ICDM Contest: TomTom Traffic Prediction for Intelligent GPS Navigation came to an end. As promised, we publish descriptions of top solutions, provided by participants. Although the reports had to be brief, the authors not only revealed a good deal of important details about their approaches, but also kept the descriptions straightforward and concise, giving all of us an unprecedented opportunity to learn the essence of data mining know-how. This is a good supplement to fully scientific articles that will be presented during Contest Workshop at the ICDM conference in Sydney.

Today, we publish descriptions for Task 1, “Traffic”. In the nearest days we’ll make another post with Task 2 and 3 reports – stay tuned! We thank all the authors for their contributions.

If you want to ask the authors any questions, feel free to comment below the post.

* * *

By Alexander Groznetsky (alegro), the winner. Alex is an experienced data miner who had participated (nick orgela) in the Netflix Prize contest in its early days – this fact becomes pretty clear when you look at the list of algorithms used by him for ICDM – they sound very Netflix-like :). To learn about the task, see “Traffic” task description page.

Read more of this post

%d bloggers like this: