Fast Generation of Best Interval Patterns for Nonmonotonic Constraints

Aleksey Buzmakov (1) , Sergei O. Kuznetsov (2) , Amedeo Napoli (1)

1 ORPAILLEUR - Knowledge representation, reasonning
2 Department of Applied Mathematics - State University Higher School of Economics

In pattern mining, the main challenge is the exponential explosion of the set of patterns. Typically, to solve this problem, a constraint for pattern selection is introduced. One of the first constraints proposed in pattern mining is support (frequency) of a pattern in a dataset. Frequency is an anti-monotonic function, i.e., given an infrequent pattern, all its superpatterns are not frequent. However, many other constraints for pattern selection are neither monotonic nor anti-monotonic, which makes it difficult to generate patterns satisfying these constraints. In this paper we introduce the notion of "generalized monotonicity" and Sofia algorithm that allow generating best patterns in polynomial time for some nonmonotonic constraints modulo constraint computation and pattern extension operations. In particular, this algorithm is polynomial for data on itemsets and interval tuples. In this paper we consider stability and delta-measure which are nonmonotonic constraints and apply them to interval tuple datasets. In the experiments, we compute best interval tuple patterns w.r.t. these measures and show the advantage of our approach over postfiltering approaches.

Mots clés: Interval tuple data; Pattern mining; Nonmonotonic constraints

Domaines: Intelligence artificielle [cs.AI]

Aleksey Buzmakov, Sergei O. Kuznetsov, Amedeo Napoli. Fast Generation of Best Interval Patterns for Nonmonotonic Constraints. Machine Learning and Knowledge Discovery in Databases, Sep 2015, Porto, Portugal. pp.157-172, ⟨10.1007/978-3-319-23525-7_10⟩. ⟨hal-01186718⟩