Abstract Representations and Frequent Pattern Discovery
This work addresses the frequent pattern mining problem for data mining researchers, presenting a theoretical generalization without clear incremental or broad impact claims.
The paper tackles the frequent pattern mining problem by generalizing it through abstract representations and casting it into algorithmic information theory, resulting in a simple algorithm to mine all frequent patterns.
We discuss the frequent pattern mining problem in a general setting. From an analysis of abstract representations, summarization and frequent pattern mining, we arrive at a generalization of the problem. Then, we show how the problem can be cast into the powerful language of algorithmic information theory. This allows us to formulate a simple algorithm to mine for all frequent patterns.