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The School of Data Analysis and Artificial Intelligence was created in 2014 as part of the Department of Data Analysis and Artificial Intelligence. The school consists of world-renowned researchers who actively participate in international research projects in the fields of data mining, formal concept analysis, semantic technologies and ontology engineering, multi-modal clustering, machine learning, natural language processing, development of intelligent and recommender systems, social network analysis, and medical informatics.
Alexey Buzmakov, doctoral student of the School of Data Analysis and Artificial Intelligence (Academic Supervisor - Sergei Kuznetsov) presented the paper ‘Fast Generation of Best Interval Patterns for Nonmonotonic Constraints’.
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 not (anti-)monotonic, which makes it difficult to generate patterns satisfying these constraints. In this paper we introduce the notion of projection-antimonotonicity and θ-$\Sigma\o\phi\iota\alpha$ algorithm that allows efficient generation of the best patterns for some nonmonotonic constraints. In this paper we consider stability and Δ-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.