Supervised Descriptive Pattern Mining

This book provides a general and comprehensible overview ofsupervised descriptive pattern mining, considering classicalgorithms and those based on heuristics. It providessome formal definitions and a general idea about patterns, pattern mining,the usefulness of patterns in the knowledge discovery process, as well as abrief summary on the tasks related to supervised descriptive patternmining. It also includes a detailed description on the tasks usually groupedunder the term supervised descriptive pattern mining: subgroups discovery,contrast sets and emerging patterns. Additionally, this book includes twotasks, class association rules and exceptional models, that are alsoconsidered within this field. A major feature of this book is that it provides a general overview (formaldefinitions and algorithms) of all the tasks included under the termsupervised descriptive pattern mining.It considers the analysis of different algorithms either based on heuristics or based on exhaustive search methodologies forany of these tasks. This book also illustrates how important these techniques arein different fields, a set of real-world applications are described. Last but not least, some related tasks are also considered and analyzed.The final aim of this book is to provide a general review of the superviseddescriptive pattern mining field, describing its tasks, its algorithms, its applications,and related tasks (those that share some common features). This book targets developers, engineers and computer scientists aiming to apply classic and heuristic-basedalgorithms to solve different kinds of pattern mining problems and apply them to real issues.Students and researchers working in this field, can use this comprehensive book (which includes its methodsand tools) as a secondary textbook.