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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.
Data Mining in Bioinformatics
Written especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.
Protein Structure Prediction
This book covers elements of both the data-driven comparative modeling approach to structure prediction and also recent attempts to simulate folding using explicit or simplified models. Despite the unsolved mystery of how a protein folds, advances are being made in predicting the interactions of proteins with other molecules. Also rapidly advancing are the methods for solving the inverse folding problem, the problem of finding a sequence to fit a structure. This book focuses on the various computational methods for prediction, their successes and their limitations, from the perspective of their most well known practitioners.
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®.
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 for Scientific and Engineering Applications
Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.
Data Mining and Data Warehousing
Provides a comprehensive textbook covering theory and practical examples for a course on data mining and data warehousing.
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.
XML Data Mining
"This book is a collection of knowledge from experts of database, information retrieval, machine learning, and knowledge management communities in developing models, methods and systems for XML data mining that can be used to address key issues and challenges in XML data mining"--Provided by publisher.
