1. The MiningZinc Framework for Constraint-Based Itemset Mining
- Author
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Guns, Tias, Dries, Anton, Tack, Guido, Nijssen, Siegfried, De Raedt, Luc, The 13th International Conference on Data Mining, Demo Track, UCL - SST/ICTM/INGI - Pôle en ingénierie informatique, Data Analytics Laboratory, Business technology and Operations, Electromobility research centre, Ding, W, Washio, T, Xiong, H, Karypis, G, Thuraisingham, B, Cook, D, and Wu, X
- Subjects
constraint programming ,Data stream mining ,Computer science ,business.industry ,Concept mining ,Constraint satisfaction ,computer.software_genre ,Inductive programming ,Constraint (information theory) ,Text mining ,framework ,High-level programming language ,Constraint-based mining ,Constraint programming ,Data mining ,Fifth-generation programming language ,business ,computer ,Software ,Declarative programming - Abstract
We present MiningZinc, a novel system for con- straint-based pattern mining. It provides a declarative approach to data mining, where a user specifies a problem in terms of constraints and the system employs advanced techniques to efficiently find solutions. Declarative programming and modeling are common in artificial intelligence and in database systems, but not so much in data mining; by building on ideas from these communities, MiningZinc advances the state-of-the-art of declarative data mining significantly. Key components of the MiningZinc system are (1) a high-level and natural language for formalizing constraint-based itemset mining problems in models, and (2) an infrastructure for executing these models, which supports both specialised mining algorithms as well as generic constraint solving systems. A use case demonstrates the generality of the language, as well as its flexibility towards adding and modifying constraints and data, as well as the use of different solution methods. ispartof: pages:1081-1084 ispartof: 13th IEEE International Conference on Data Mining Workshops pages:1081-1084 ispartof: Demo track at the IEEE International Conference on Data Mining location:Dallas, Texas, USA date:7 Dec - 10 Dec 2013 status: published
- Published
- 2013