This book provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, pre-processing, processing, and warehousing data. It then presents information about data warehouses,...

Das Buch lehrt Data-Mining-Konzepte und sein Inhalt ist nicht mit einer Programmiersprache verbunden. Daher kann man das Buch unabhängig davon verwenden, ob man Data-Mining-Konzepte lernen/unterrichten möchte oder nicht. Das Buch behandelt wichtige Themen wie Datenvorverarbeitung, Klassifizierung, Clustering, unausgewogene Daten, Erkennung von Anomalien (d. h. verrauschte Daten, Beobachtungen, die nicht in die Normalverteilung passen, Ausreißer usw.) mithilfe von statistischen, Clustering- und anderen Techniken.

Le livre enseigne les concepts d'exploration de données et son contenu n'est associé à aucun langage de programmation. On peut donc adopter ce livre indépendamment du fait que l'on veuille apprendre/enseigner des concepts de data mining, etc. Le livre couvre des sujets importants tels que le prétraitement des données, la classification, le regroupement, les données déséquilibrées, la détection des anomalies (c'est-à-dire les données bruitées, les observations qui ne correspondent pas à la distribution normale, les valeurs aberrantes, etc).

The book teaches data mining concepts and its content is not associated with any programming language. Hence one can adopt the book regardless whether they want to learn/teach data mining concepts etc. This also means that you will not see any case studies or exciting applications of data mining concepts in this book.The book covers important topics such as data pre-processing, classification, clustering, imbalanced data, anomaly detection (i.e. noisy data, observations that do not fit the normal distribution, outliers etc.) using statistical, clustering and other techniques.