Mining sequential patterns with flexible constraints from MOOC data

Abstract

Online learning is playing an increasingly important role in education. Massive open online course (MOOC) platforms are among the most important tools in online learning, and record historical learning data from an extremely large number of learners. To enhance the learning experience, a promising approach is to apply sequential pattern mining (SPM) to discover useful knowledge in these data. In this paper, mining sequential patterns (SPs) with flexible constraints in MOOC enrollment data is proposed, which follows that research approach. Three constraints are proposed: the length constraint, discreteness constraint, and validity constraint. They are used to describe the effect of the length of enrollment sequences, variance of enrollment dates, and enrollment moments, respectively. To improve the mining efficiency, the three constraints are pushed into the support, which is the most typical parameter in SPM, to form a new parameter called support with flexible constraints (SFC). SFC is proved to satisfy the downward closure property, and two algorithms are proposed to discover SPs with flexible constraints. They traverse the search space in a breadth-first and depth-first manner. The experimental results demonstrate that the proposed algorithms effectively reduce the number of patterns, with comparable performance to classical SPM algorithms.

Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data

“…Since the first FIM algorithm, Apriori [2], many such algorithms have been presented in the last two decades. These algorithms are classified into different types, such as sequential patterns mining algorithms [24,25], data stream mining algorithms [26,27], graph mining algorithms [28,29], approximate frequent itemset mining in uncertain data [30,31], and high utility frequent itemset mining algorithms [32,33]. In this section, we present the FIM algorithms relevant to the research presented in this paper.…”

Hacia el desarrollo de un sistema de evaluación adaptativa computarizada basada en reglas de asociación secuenciales

“…Tabla 1: Trabajos relacionados. Artículos 1 2 3 4 5 (Tarus et al, 2018) X X (Anwar y Uma, 2022) X (Zheng et al, 2019) X X (Wong et al, 2019) X X (Liu et al, 2018) X X X (Qu et al, 2019) X X (Klašnja-Milićević et al, 2018) X X (Zhu et al, 2019) (Deeva y De Weerdt, 2019) (Taub et al, 2018) X X (Wan y Niu, 2020) X X (Al-Twijri et al, 2022) (Taub y Azevedo, 2018b) X X (Norm Lien et al, 2020) X X (Yildirim y Usluel, 2022) X X X (Zhang et al, 2022) X X X (Shih, 2018) X X (Song et al, 2022) X X (Wang y Zaïane, 2018) (Mudrick et al, 2018) X X (Taub y Azevedo, 2018a) X X (Yang, 2021) (Bermudez et al, 2020) (Cheng Tan et al, 2020) X X (Malekian et al, 2020) X X (Kong y Pollock, 2020) X X (Latypova, 2022) X X X (Fatahi et al, 2018) X X X (Pogorskiy y Beckmann, 2022) X (He et al, 2021) X (Chen y Wang, 2020) X (Czibula et al, 2019) X X (Doko et al, 2018) X X (Real et al, 2021) X (Song y Ye, 2021) (Cheng et al, 2021) X (Niemeijer et al, 2020) X (Aktas y Aktas, 2021)…”

“…),Song et al(2022) yHe et al (2021) están públicamente disponibles, dichos resultados se muestran en la Figura 5.…”

Discovering Significant Sequential Patterns in Data Stream by an Efficient Two-Phase Procedure

“…Episode sequential pattern mining [19] is used to look for patterns in a single sequence, rather than a group of sequences. Periodic sequential pattern mining [20,21] is used to fnd patterns that occur frequently and periodically in long sequences. Subgraph mining [22,23] is another feld of sequential pattern mining, which aims to discover all frequent subgraphs in graph databases, the corresponding algorithms based on diferent data structures (such as liststructure [24], pattern-tree [25], and optimization algorithm [19]) are proposed to solve the related pattern mining problem from sequences database, and all these pattern mining approaches are based on the sequential database and the constraints threshold (selected by the user).…”

“…For s � L to U do (7) num � r(s, n 1 ) (8) p � P B (X)//by Lemma 3 (9) total+ � num (10) P.add (<X, p, and num>) (11) End for (12) End for (13) End for (14) For each FSSP candidate X do (15) px � P B (X) (16) For each item Pitem in P do (17) If Pitem.p < px then (18) number+ � Pitem.num (19) End If (20)…”