k-Pattern Set Mining under Constraints

Guns, Tias; Nijssen, Siegfried; Raedt, Luc De

doi:10.1109/tkde.2011.204

Cited by 71 publications

(97 citation statements)

References 31 publications

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“…For example, Listing 13 shows the specification of concept learning in a k-pattern set mining setting [24]. The goal is to find the k patterns that together best describe the fraudulent transactions, while covering 9 c o n s t r a i n t c a r d ( A u t h o r s ) >= Alpha ; 10 s o l v e s a t i s f y : : e n u m e r a t e ; few other transactions.…”

Section: Mining Pattern Setsmentioning

confidence: 99%

“…Other problems studied in the context of k-pattern set mining and n-ary pattern mining [24,25] can be formulated in a similar high-level way.…”

Section: Mining Pattern Setsmentioning

confidence: 99%

“…Pattern sets can have many symmetric solutions [24], which can slow down search. Symmetry breaking constraints can be added to overcome this, for example by lexicographically ordering the itemsets.…”

Section: Mining Pattern Setsmentioning

confidence: 99%

“…However, these approaches were low-level and solver dependent. The use of higher-level modeling languages and primitives has been studied before [43,24], though again tied to one particular solving technology. MiningZinc on the other hand enables the use of both general constraint solvers and highly optimized mining algorithms.…”

Section: Related Workmentioning

confidence: 99%

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MiningZinc: A declarative framework for constraint-based mining

Guns¹,

Dries²,

Nijssen³

et al. 2017

Artificial Intelligence

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We introduce MiningZinc, a declarative framework for constraint-based data mining. MiningZinc consists of two key components: a language component and an execution mechanism.First, the MiningZinc language allows for high-level and natural modeling of mining problems, so that MiningZinc models are similar to the mathematical definitions used in the literature. It is inspired by the Zinc family of languages and systems and supports user-defined constraints and functions.Secondly, the MiningZinc execution mechanism specifies how to compute solutions for the models. It is solver independent and supports both standard constraint solvers and specialized data mining systems. The high-level problem specification is first translated into a normalized constraint language (FlatZinc). Rewrite rules are then used to add redundant constraints or solve subproblems using specialized data mining algorithms or generic constraint programming solvers. Given a model, different execution strategies are automatically extracted that correspond to different sequences of algorithms to run. Optimized data mining algorithms, specialized processing routines and generic solvers can all be automatically combined.Thus, the MiningZinc language allows one to model constraint-based itemset mining problems in a solver independent way, and its execution mechanism can automatically chain different algorithms and solvers. This leads to a unique combination of declarative modeling with high-performance solving.

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Section: Mining Pattern Setsmentioning

confidence: 99%

“…Other problems studied in the context of k-pattern set mining and n-ary pattern mining [24,25] can be formulated in a similar high-level way.…”

Section: Mining Pattern Setsmentioning

confidence: 99%

Section: Mining Pattern Setsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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MiningZinc: A declarative framework for constraint-based mining

Guns¹,

Dries²,

Nijssen³

et al. 2017

Artificial Intelligence

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show abstract

“…Recent work [16,17] has proposed to use Constraint Programming for conceptual clustering. The problem is then formalized as the search of frequent, pairwise non overlapping k-patterns that cover the whole dataset.…”

Section: Related Workmentioning

confidence: 99%

A Declarative Framework for Constrained Clustering

Dao

Duong

Vrain

2013

Machine Learning and Knowledge Discovery in Databases

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Abstract. In recent years, clustering has been extended to constrained clustering, so as to integrate knowledge on objects or on clusters, but adding such constraints generally requires to develop new algorithms. We propose a declarative and generic framework, based on Constraint Programming, which enables to design clustering tasks by specifying an optimization criterion and some constraints either on the clusters or on pairs of objects. In our framework, several classical optimization criteria are considered and they can be coupled with different kinds of constraints. Relying on Constraint Programming has two main advantages: the declarativity, which enables to easily add new constraints and the ability to find an optimal solution satisfying all the constraints (when there exists one). On the other hand, computation time depends on the constraints and on their ability to reduce the domain of variables, thus avoiding an exhaustive search.

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Finding Probabilistic Rule Lists using the Minimum Description Length Principle

et al. 2018

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An important task in data mining is that of rule discovery in supervised data. Well-known examples include rule-based classification and subgroup discovery. Motivated by the need to succinctly describe an entire labeled dataset, rather than accurately classify the label, we propose an MDL-based supervised rule discovery task. The task concerns the discovery of a small rule list where each rule captures the probability of the Boolean target attribute being true. Our approach is built on a novel combination of two main building blocks: (i) the use of the Minimum Description Length (MDL) principle to characterize good-andsmall sets of probabilistic rules, (ii) the use of branch-and-bound with a best-first search strategy to find better-than-greedy and optimal solutions for the proposed task. We experimentally show the effectiveness of our approach, by providing a comparison with other supervised rule learning algorithms on real-life datasets.

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k-Pattern Set Mining under Constraints

Cited by 71 publications

References 31 publications

MiningZinc: A declarative framework for constraint-based mining

MiningZinc: A declarative framework for constraint-based mining

A Declarative Framework for Constrained Clustering

Finding Probabilistic Rule Lists using the Minimum Description Length Principle

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