David J. Hand est un mathématicien et auteur distingué dont le travail explore les principes de la probabilité et de l'analyse des données. Son expertise couvre un large éventail de sujets, de la classification et de l'exploration de données aux fondements de la statistique. À travers ses publications, il examine comment les modèles statistiques influencent notre perception du monde et comment découvrir des événements apparemment improbables. L'approche de Hand repose sur une profonde compréhension des principes mathématiques et de leur application aux phénomènes du monde réel.
Statistics has evolved into an exciting discipline which uses deep theory and powerful software to shed light on the world around us: from clinical trials in medicine, to economics, sociology, and countless other subjects vital to understanding modern life. This Very Short Introduction explores and explains how statistics works today.
The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.