conference · 2004
Mining Patterns Using Relaxations of User Defined Constraints
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- TL;DR
- Sequential pattern mining often struggles with a lack of focus on user expectations and an overwhelming number of discovered patterns.
- Problem
- While using constraints is a common solution, it tends to turn the mining process into a rigid hypothesis-testing task.
- Method
- We propose a new methodology that uses constraint relaxations to filter accepted patterns during the mining process, keeping the focus on user expectations without compromising the discovery of unknown patterns.
- Results
- Not specified in the abstract.
- Contributions
- Not specified in the abstract.
- Limitations
- Not specified in the abstract.
- Takeaways
- The approach introduces a hierarchy of relaxations specifically applied to constraints expressed as context-free languages.
- Applications
- Not specified in the abstract.
- Topics
- Sequential Pattern Mining; User-Defined Constraints; Constraint Relaxation; Context-Free Languages
- For industry
- Not specified in the abstract.
- Why it matters
- Not specified in the abstract.
Abstract
Abstract. The main drawbacks of sequential pattern mining have been its lack of focus on user expectations and the high number of discovered patterns. However, the solution commonly accepted – the use of constraints – approximates the mining process to a hypothesis-testing task. In this paper, we propose a new methodology to mine sequential patterns, keeping the focus on user expectations, without compromising the discovery of unknown patterns. Our methodology is based on the use of constraint relaxations, and it consists on using them to filter accepted patterns during the mining process. We propose a hierarchy of relaxations, applied to constraints expressed as context-free languages. 1