Large corpora of formal proofs have been developed over the years, that sit in the repositories of the various existing proof assistants. We advocate that these databases could be analysed by means of data-mining techniques, to help both the improvement of these tools on their usage, and the proof automation they provide. We suggest a technique for the analysis, frequent subtree mining, review ...

The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new model for the effective extraction of generalized association rules form a XML document collection. We directly use frequent subtree mining techniques in the disco...

Predictive graph mining approaches in chemical databases are extremely popular and effective. Most of these approaches first extract frequent sub-graphs and then use them as features to build predictive models. In the work presented here, the approach taken is similar. However, instead of frequent sub-graphs, frequent trees, based on SMILES strings are derived. For this, the SMILES strings of c...

Given a unitary representation T of a finite group G in C, write M for the variety of such representations which are unitary equivalent to T . The representation T is said to be frequent if the dimension of the variety M is maximal (among all representations of G in the same complex space). We prove that the irreducible representations are distributed, in the frequent representation (of large d...

Mining frequent subtrees from databases of labeled trees is a new research field that has many practical applications in areas such as computer networks, Web mining, bioinformatics, XML document mining, etc. These applications share a requirement for the more expressive power of labeled trees to capture the complex relations among data entities. Although frequent subtree mining is a more diffic...

The bridging of the gap between 1) subsymbolic pattern recognition and learning algorithms and 2) symbolic reasoning algorithms, has been a major issue for AI since the early days of the field. One class of approaches involves integrating subsymbolic and symbolic systems, but this raises the question of how to effectively translate between the very different languages involved. In the approach ...