Abstract Representations and Frequent Pattern Discovery

TL;DR

This paper explores a generalized approach to frequent pattern mining by leveraging abstract representations and algorithmic information theory, resulting in a simple, comprehensive algorithm for discovering all frequent patterns.

Contribution

It introduces a novel generalization of the frequent pattern mining problem and formulates it within the framework of algorithmic information theory, enabling a new mining algorithm.

Findings

Unified framework for pattern mining and abstraction

Algorithmic information theory-based formulation

Efficient method for mining all frequent patterns

Abstract

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.