This book describes the important ideas in a variety of fields such as medicine‎,‎ biology‎,‎ finance‎,‎ and marketing in a common conceptual framework‎.‎ While the approach is statistical‎,‎ the emphasis is on concepts rather than mathematics‎.‎ Many examples are given‎,‎ with a liberal use of colour graphics‎.‎ It is a valuable resource for statisticians and anyone interested in data mining in science or industry‎.‎ The book‎'‎s coverage is broad‎,‎ from supervised learning ‎(‎prediction‎)‎ to unsupervised learning‎.‎ The many topics include neural networks‎,‎ support vector machines‎,‎ classification trees and boosting‎-‎-‎-the first comprehensive treatment of this topic in any book‎.‎This major new edition features many topics not covered in the original‎,‎ including graphical models‎,‎ random forests‎,‎ ensemble methods‎,‎ least angle regression & path algorithms for the lasso‎,‎ non‎-negative matrix factorisation‎,‎ and spectral clustering‎.‎ There is also a chapter on methods for ‎"‎wide‎''‎ data ‎(‎p bigger than n‎)‎‎,‎ including multiple testing and false discovery rates‎.‎