Explainability is a fundamental principle in the field of Artificial Intelligence (AI), ensuring that AI models and systems are understandable and transparent to end-users. Specifically, it tackles the challenge of providing explanations for AI predictions. In interpretable machine learning, classification rules are regarded one of the most well-known explainability techniques, due to their expressive power and transparent structure. In this paper, we first show that computing classification rules is equivalent to mining disjunctive patterns from the corresponding transaction database. Second, we show that our approach provides a clear characterization of optimal classification rules, wherein disjunctive patterns satisfy the non-redundancy property in the target class and such patterns correspond to minimal generators in this class. Then, we propose a SAT-based solution of the problem for computing optimal classification rules using MaxSAT solvers, for which the optimality is a balancing between the accuracy and the size of the rules. Finally, we present an empirical evaluation on several representative datasets, showing that our approach achieves good performance in terms of accuracy and interpretability compared to existing baselines.

The computation of minimal rare itemsets is a well known task in data mining, with numerous applications, e.g., drugs effects analysis and network security, among others. This paper presents a novel approach to the computation of minimal rare itemsets. First, we introduce a generalization of the traditional minimal rare itemset model called k-minimal rare itemset. A k-minimal rare itemset is defined as an itemset that becomes frequent or rare based on the removal of at least k or at most (k − 1) items from it. We claim that our work is the first to propose this generalization in the field of data mining. We then present a SAT-based framework for efficiently discovering k-minimal rare itemsets from large transaction databases. Afterwards, by partitioning the k-minimal rare itemset mining problem into smaller sub-problems, we aim to make it more manageable and easier to solve. Finally, to evaluate the effectiveness and efficiency of our approach, we conduct extensive experimental analysis using various popular datasets. We compare our method with existing specialized algorithms and CP-based algorithms commonly used for this task.

Mining interesting patterns from data is a core part of the data mining world. High utility mining, an active research topic in data mining, aims to discover valuable itemsets with high profit (e.g., cost, risk). However, the measure of interest of an itemset must primarily reflect not only the importance of items in terms of profit, but also their occurrence in data in order to make more crucial decisions. Some proposals are then introduced to deal with the problem of computing high utility itemsets that meet a minimum support threshold. However, in these existing proposals, all transactions in which the itemset appears are taken into account, including those in which the itemset has a low profit. So, no additional information about the overall utility of the itemset is taken into account. This paper addresses this issue by introducing a SAT-based model to efficiently find the set of all frequent high utility itemsets with the use of a minimum utility threshold applied to each transaction in which the itemset appears. More specifically, we reduce the problem of mining frequent high utility itemsets to the one of enumerating the models of a formula in propositional logic, and then we use state-of-the-art SAT solvers to solve it. Afterwards, to make our approach more efficient, we provide a decomposition technique that is particularly suitable for deriving smaller and independent sub-problems easy to resolve. Finally, an extensive experimental evaluation on various popular datasets shows that our method is fast and scale well compared to the state-of-the art algorithms.

Computing high utility motifs is a fundamental data mining method for discovering useful itemsets yielding high utility values. Minimal and maximal high utility itemsets are two examples of compact representations used to reduce the output size due to the large and incomprehensible number of patterns. In this paper, we present a novel method for mining minimal and maximal high utility itemsets using propositional satisfiability. First, we show that minimal and maximal high utility patterns are X-minimal models of a CNF formula. Then, to improve the scalability issue of our method, we harness a decomposition paradigm that splits the transaction database into smaller and independent transaction sub-bases, allowing an efficient enumeration of minimal and maximal high utility itemsets. Finally, through extensive evaluation studies on various real-world datasets, we demonstrate that our approach is very competitive w.r.t. to the state-of-the-art specialized solutions.

Gradual itemsets model complex attributes covariations of the form the more or less is A, the more or less is B. Recently, such kind of itemsets has received great attention over the last years, and several proposals have been introduced to automatically extract these patterns from numerical databases. Unfortunately, discovering such itemsets remains challenging because of the exponential combinatorial search space.In this paper, we first formalize the problem of mining gradual itemsets as a constraint-based problem. Then, we use SAT solvers for solving the corresponding propositional satisfiability problem. Extensive experiments on real-world datasets confirm that our proposal is competitive with GRITE, one of the most efficient state-of-the-art algorithm for discovering frequent gradual itemsets. Lastly, we show the flexibility of our SAT-based approach by its ability to modeling additional user constraints without revising the solving process.