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Frequent itemset mining (FIM) is an essential task within data analysis since it is responsible for extracting frequently occurring events, patterns, or items in data. Insights from such pattern analysis offer important benefits in decision‐making processes. However, algorithmic solutions for mining such kind of patterns are not straightforward since the computational complexity exponentially increases with the number of items in data. This issue, together with the significant memory consumption that is present in the mining process, makes it necessary to propose extremely efficient solutions. Since the FIM problem was first described in the early 1990s, multiple solutions have been proposed by considering centralized systems as well as parallel (shared or nonshared memory) architectures. Solutions can also be divided into exhaustive search and nonexhaustive search models. Many of such approaches are extensions of other solutions and it is therefore necessary to analyze how this task has been considered during the last decades. This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules

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This paper presents a proposal for the extraction of association rules called G3PARM (Grammar-Guided Genetic Programming for Association Rule Mining) that makes the knowledge extracted more expressive and flexible. This algorithm allows a context-free grammar to be adapted and applied to each specific problem or domain and eliminates the problems raised by discretization. This proposal keeps the best individuals (those that exceed a certain threshold of support and confidence) obtained with the passing of generations in an auxiliary population of fixed size n. G3PARM obtains solutions within specified time limits and does not require the large amounts of memory that the exhaustive search algorithms in the field of association rules do. Our approach is compared to exhaustive search (Apriori and FP-Growth) and genetic (QuantMiner and ARMGA) algorithms for mining association rules and performs an analysis of the mined rules. Finally, a series of experiments serve to contrast the scalability of our algorithm. The proposal obtains a small set of rules with high support and confidence, over 90 and 99% respectively. Moreover, the resulting set of rules closely satisfies all the dataset instances. These results illustrate that our proposal is highly promising for the discovery of association rules in different types of datasets.

This paper proposes the application of association rule mining to improve quizzes and courses. First, the paper shows how to preprocess quiz data and how to create several data matrices for use in the process of knowledge discovery. Next, the proposed algorithm that uses grammar‐guided genetic programming is described and compared with both classical and recent soft‐computing association rule mining algorithms. Then, different objective and subjective rule evaluation measures are used to select the most interesting and useful rules. Experiments have been carried out by using real data of university students enrolled on an artificial intelligence practice Moodle's course on the CLIPS programming language. Some examples of these rules are shown, together with the feedback that they provide to instructors making decisions about how to improve quizzes and courses. Finally, starting with the information provided by the rules, the CLIPS quiz and course have been updated. These innovations have been evaluated by comparing the performance achieved by students before and after applying the changes using one control group and two different experimental groups.