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Communication Dans Un Congrès Année : 2022

Delta-Closure Structure for Studying Data Distribution

Résumé

In this paper, we revisit pattern mining and study the distribution underlying a binary dataset thanks to the closure structure which is based on passkeys, i.e., minimum generators in equivalence classes robust to noise. We introduce ∆-closedness, a generalization of the closure operator, where ∆ measures how a closed set differs from its upper neighbors in the partial order induced by closure. A ∆-class of equivalence includes minimum and maximum elements and allows us to characterize the distribution underlying the data. Moreover, the set of ∆classes of equivalence can be partitioned into the so-called ∆closure structure. In particular, a ∆-class of equivalence with a high ∆ is supported by more observations and thus is more stable. In the experiments, we study the ∆-closure structure of several real-world datasets and show that this structure is very stable for large ∆ and does not substantially depend on the data sampling used for the analysis.
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Dates et versions

hal-04055185 , version 1 (01-04-2023)

Identifiants

Citer

Aleksey Buzmakov, Sergei O Kuznetsov, Tatiana Makhalova, Amedeo Napoli. Delta-Closure Structure for Studying Data Distribution. IEEE International Conference on Data Mining, ICDM 2022, Orlando, FL, USA, November 28 - Dec. 1, 2022, Xingquan Zhu, Sanjay Ranka, My T. Thai, Takashi Washio, and Xindong Wu editors, Nov 2022, Orlando, FL, USA, United States. ⟨10.1109/ICDM54844.2022.00099⟩. ⟨hal-04055185⟩
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