Revisiting Frequent (Closed) Gradual Itemsets Mining

The task of mining gradual itemsets holds significant importance in pattern mining, particularly when working with numerical data. It involves the discovery of covariations between attributes in the form of “The more/less X,…, the more/less Y,” referred to as gradual itemsets. However, discovering these itemsets remains challenging, partly due to the exponential combinatorial search space involved in large-scale data processing. Consequently, existing algorithms for gradual itemset mining encounter difficulties, such as slow processing speeds, and occasional failures to terminate due to the overwhelming number of candidate itemsets requiring exploration. A large number of candidates is generated, but a large proportion of them turns out to be infrequent once their supports are computed. This paper introduces an approach to streamline this process by efficiently reducing the number of candidates for which support needs to be computed through the introduction of a stricter upper-bound criterion. By circumventing the costly support computation for numerous candidate itemsets, our approach exhibits efficiency in terms of speed when applied to real databases, including large-scale databases that pose challenges for existing algorithms. Furthermore, we establish a connection in terms of pattern coverage between the two principal gradualness semantics commonly employed in the literature.