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Survey on using constraints in data mining

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Abstract

This paper provides an overview of the current state-of-the-art on using constraints in knowledge discovery and data mining. The use of constraints in a data mining task requires specific definition and satisfaction tools during knowledge extraction. This survey proposes three groups of studies based on classification, clustering and pattern mining, whether the constraints are on the data, the models or the measures, respectively. We consider the distinctions between hard and soft constraint satisfaction, and between the knowledge extraction phases where constraints are considered. In addition to discussing how constraints can be used in data mining, we show how constraint-based languages can be used throughout the data mining process.

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Notes

  1. The basic idea of support vector machine is to represent the decision boundary using a subset of the training examples, known as support vectors. Given a set of hyperplanes, the classifier selects one hyperplane for representing its decision boundary, based on how well they are expected to perform on the example to classify.

  2. The Euclidean distance, i.e. straight-line distance is an ineffective measure in the presence of obstacles and facilitators. An obstacle is a physical object that obstructs reachability among the individuals. On the contrary, a facilitator is a physical object that enhances reachability among people.

  3. MiniZinc is a constraint-modeling language. It is sufficiently high-level to express most constraint problems easily, but low-level enough to be mapped onto existing solvers easily and consistently (Nethercote et al. 2007). The MiningZinc language (Guns et al. 2013b) cited in this section is an extension of MiniZinc for data mining.

  4. Inductive databases extend the closure principle to the knowledge discovery field. The principle simply states that the output of a query for knowledge extraction can be the input of another query of a compatible type (Imielinski and Mannila 1996).

  5. The system automatically captures the properties of such constraints (e.g. monotonicity and anti-monotonicity), to be used directly during the extraction of the mining model.

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Acknowledgments

This work was supported by the European Commission under the project “Inductive Constraint Programming (ICON)” contract number FP7-284715, and by a Grant for “Big Data Social Mining” of the University of Pisa. We warmly thank the anonymous referees for their very valuable suggestions.

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  1. Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127, Pisa, Italy

    Valerio Grossi, Andrea Romei & Franco Turini

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  1. Valerio Grossi
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Grossi, V., Romei, A. & Turini, F. Survey on using constraints in data mining. Data Min Knowl Disc 31, 424–464 (2017). https://doi.org/10.1007/s10618-016-0480-z

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