The random forest classifier is widely used in different fields due to its accuracy and
robustness. Since its invention, the random forest algorithm is naturally developed for multi …

For sequence classification, an important issue is to find discriminative features, where
sequential pattern mining (SPM) is often used to find frequent patterns from sequences as …

Sequence pattern mining aims to discover frequent subsequences as patterns in a single
sequence or a sequence database. By combining gap constraints (or flexible wildcards) …

The discovery of patterns that accurately discriminate one class label from another remains
a challenging data mining task. Subgroup discovery (SD) is one of the frameworks that …

Current decision trees such as C4. 5 and CART are widely used in different fields due to
their simplicity, accuracy and intuitive interpretation. Similar to other popular classifiers …

The assessment of behavioral rules with respect to a given dataset is key in several
research areas, including declarative process mining, association rule mining, and …

When learning sequence representations, traditional pattern-based methods often suffer
from the data sparsity and high-dimensionality problems while recent neural embedding …

Discriminative sequential pattern mining is one of the most important topics in pattern
mining, which has a very wide range of applications. Discriminative sequential pattern …

In recent years, the field of pattern mining has shifted to user-centered methods. In such a
context, it is necessary to have a tight coupling between the system and the user where …

Sequential pattern mining has been introduced by Agrawal and Srikant (in: Proceedings of
ICDE'95, pp 3–14, 1995) 2 decades ago, and its usefulness has been widely proved for …