David J. Hand is a distinguished mathematician and author whose work delves into the principles of probability and data analysis. His expertise spans a wide array of subjects, from classification and data mining to the foundations of statistics. Through his publications, he explores how statistical patterns influence our perception of the world and how to uncover seemingly improbable events. Hand's approach is rooted in a profound understanding of mathematical principles and their application to real-world phenomena.
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.