Detecting sets of relevant patterns from a given dataset is an important challenge in data
mining. The relevance of a pattern, also called utility in the literature, is a subjective measure …

Pattern mining is an essential part of knowledge discovery and data analytics. It is a
powerful paradigm, especially when combined with constraint reasoning. In this paper, we …

Mining ubiquitous sensing data is important but also challenging, due to many factors, such
as heterogeneous large-scale data that is often at various levels of abstraction. This also …

CP modeling languages offer convenience to the user by allowing both constants and
decision variables to be first class citizens over which mathematical and Boolean operators …

Pattern mining is an essential part of knowledge discovery and data analytics. It is a
powerful paradigm, especially when combined with constraint reasoning. In this overview …

Constraint-based sequential pattern mining aims at identifying frequent patterns on a
sequential database of items while observing constraints defined over the item attributes …

Sequential pattern mining remains a challenging task due to the large number of redundant
candidate patterns and the exponential search space. In addition, further analysis is still …

These last years, there has been a growing interest for structures like tables and decision
diagrams in Constraint Programming (CP). This is due to the universal character of these …

Abstract Top‐Rank‐K Frequent Itemset (or Pattern) Mining (FPM) is an important data
mining task, where user decides on the number of top frequency ranks of patterns (itemsets) …

Community detection in the presence of prior information or preferences on solution
properties is called semi-supervised or constrained community detection. The task of …