[Paper Review] Discovering Knowledge using a Constraint-based Language

Patrice Boizumault, Bruno Crémilleux+3 more|arXiv (Cornell University)|Jul 18, 2011

Data Mining Algorithms and Applications17 references3 citations

TL;DR

This paper proposes a declarative, constraint-based language for mining global patterns and pattern sets in data mining, enabling analysts to express complex clustering and association patterns through high-level constraints. By integrating constraint programming with pattern mining, the approach allows flexible, reusable queries that dynamically refine results—demonstrated through clustering variants like soft and co-clustering—yielding more meaningful, non-redundant patterns with minimal implementation overhead.

ABSTRACT

Discovering pattern sets or global patterns is an attractive issue from the pattern mining community in order to provide useful information. By combining local patterns satisfying a joint meaning, this approach produces patterns of higher level and thus more useful for the data analyst than the usual local patterns, while reducing the number of patterns. In parallel, recent works investigating relationships between data mining and constraint programming (CP) show that the CP paradigm is a nice framework to model and mine such patterns in a declarative and generic way. We present a constraint-based language which enables us to define queries addressing patterns sets and global patterns. The usefulness of such a declarative approach is highlighted by several examples coming from the clustering based on associations. This language has been implemented in the CP framework.

Motivation & Objective

• To address the challenge of excessive redundancy and fragmented insights in traditional local pattern mining.
• To provide a generic, declarative framework for discovering higher-level global patterns by combining multiple local patterns.
• To enable data analysts to easily refine queries and explore diverse clustering models (e.g., soft, co-clustering) using constraint logic.
• To reduce the need for custom algorithm development per new pattern type by offering a unified, extensible modeling language.
• To improve the relevance and interpretability of mined patterns through constraint-driven refinement and optimization.

Proposed method

• The approach uses a constraint-based language built on terms (constants, variables, functions, operators) to express pattern queries declaratively.
• Key constraints include frequency, closure, coverage of transactions and items, overlap limits on transactions/items, and canonical ordering to break symmetries.
• Queries are formulated as logical conjunctions of constraints, allowing modular and composable specification of mining tasks.
• The framework leverages existing Constraint Programming (CP) solvers to efficiently search for solutions satisfying all specified constraints.
• The language supports incremental query refinement—e.g., adding overlap or size constraints—to explore different clustering semantics.
• The approach is demonstrated on association-based clustering tasks, with queries adapted to soft, co-, and soft co-clustering via constraint modifications.

Experimental results

Research questions

• RQ1 How can a declarative, constraint-based language simplify the discovery of global patterns from local patterns in data mining?
• RQ2 Can constraint programming effectively model and solve complex clustering tasks such as soft and co-clustering in a generic way?
• RQ3 To what extent can query refinements in a constraint language reduce redundancy and improve the interpretability of mined patterns?
• RQ4 How does the integration of domain-specific constraints (e.g., overlap limits, coverage) enhance the quality of clustering results?
• RQ5 Can this framework be extended to support new data mining tasks without rewriting the underlying algorithm?

Key findings

• The constraint-based language enables the specification of complex clustering tasks—such as soft and co-clustering—through simple, incremental query refinements.
• For the soft clustering query (Q4) with k=3 and δT=1, 13 initial solutions were found, which reduced to 8 after filtering non-frequent patterns and to 1 after filtering small patterns.
• The single remaining solution after filtering (s′9) matched the solution of the original hard clustering problem, validating the approach’s correctness and consistency.
• The canonical constraint reduced the number of symmetric solutions from 78 to 13, demonstrating its effectiveness in symmetry breaking.
• The framework successfully generated distinct clusterings for co-clustering (Q5) and soft co-clustering (Q6), showing its flexibility across different clustering semantics.
• The approach enables rapid exploration of diverse clustering models through declarative constraint specification, significantly reducing implementation overhead.