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Data Mining and Machine Learning
New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.
Data Mining and Analysis
A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.
Demand-Driven Associative Classification
The ultimate goal of machines is to help humans to solve problems. Such problems range between two extremes: structured problems for which the solution is totally defined (and thus are easily programmed by humans), and random problems for which the solution is completely undefined (and thus cannot be programmed). Problems in the vast middle ground have solutions that cannot be well defined and are, thus, inherently hard to program. Machine Learning is the way to handle this vast middle ground, so that many tedious and difficult hand-coding tasks would be replaced by automatic learning methods. There are several machine learning tasks, and this work is focused on a major one, which is known as classification. Some classification problems are hard to solve, but we show that they can be decomposed into much simpler sub-problems. We also show that independently solving these sub-problems by taking into account their particular demands, often leads to improved classification performance.
Data Mining and Data Warehousing
Provides a comprehensive textbook covering theory and practical examples for a course on data mining and data warehousing.
Data-Driven Science and Engineering
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Artificial Intelligence and Legal Analytics
This book describes how text analytics and computational models of legal reasoning will improve legal IR and let computers help humans solve legal problems.
DATA MINING
Data Mining is an emerging technology that has made its way into science, engineering, commerce and industry as many existing inference methods are obsolete for dealing with massive datasets that get accumulated in data warehouses. This comprehensive and up-to-date text aims at providing the reader with sufficient information about data mining methods and algorithms so that they can make use of these methods for solving real-world problems. The authors have taken care to include most of the widely used methods in data mining with simple examples so as to make the text ideal for classroom learning. To make the theory more comprehensible to the students, many illustrations have been used, and ...
Introduction to Semi-Supervised Learning
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mi...
Machine Learning Refined
An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.
Sharing America's Neighborhoods
The first part of this book presents a fresh and encouraging report on the state of racial integration in America's neighborhoods. It shows that while the majority are indeed racially segregated, a substantial and growing number are integrated, and remain so for years. Still, many integrated neighborhoods do unravel quickly, and the second part of the book explores the root causes. Instead of panic and white flight causing the rapid breakdown of racially integrated neighborhoods, the author argues, contemporary racial change is driven primarily by the decision of white households not to move into integrated neighborhoods when they are moving for reasons unrelated to race. Such white avoidanc...
