Towards multi-core parallel fuzzy tree mining

Fuzzy tree mining has been recently introduced in order to extract frequent subtrees from databases of labeled trees. It has many applications, especially for handling semi-structured data (e.g., XML). In this framework, soft approaches have been proposed, also known as fuzzy tree mining. They allow the methods to better recognize patterns that are embedded in the database, even if the patterns are only partially present. However, such soft methods have to cope with the problem of remaining scalable on huge volumes of data, regarding both time and memory consumption. It is thus interesting to take advantage of the new generations of computers with multi-core architectures. We thus propose an original method for parallelizing fuzzy tree mining. This paper presents our approach and discusses the main problems addressed and solutions proposed, based on the experimental results.