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Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R!)

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Data Mining and Anlaytics are the foundation technologies for the new knowledge based world where we build models from data and databases to understand and explore our world. Data mining can improve our business, improve our government, and improve our life and with the right tools, any one can begin to explore this new technology, on the path to becoming a data mining professional. This book aims to get you into data mining quickly. Load some data (e.g., from a database) into the Rattle toolkit and within minutes you will have the data visualised and some models built. This is the first step in a journey to data mining and analytics. The book encourages the concept of programming by example and programming with data - more than just pushing data through tools, but learning to live and breathe the data, and sharing the experience so others can copy and build on what has gone before. It is accessible to many readers and not necessarily just those with strong backgrounds in computer science or statistics. Details of some of the more popular algorithms for data mining are very simply and, more importantly, clearly explained. Technology for transforming a database through data mining and machine learning into knowledge is now readily accessible.
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Applied Data Mining
Applied Data Mining

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Introduction to Probability and Statistics Using R
Introduction to Probability and Statistics Using R
This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three... More > semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors....
Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)
Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)

Time Series Analysis and Its Applications presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception...


Optimization Algorithms for Networks and Graphs
Optimization Algorithms for Networks and Graphs
A revised and expanded advanced-undergraduate/graduate text (first ed., 1978) about optimization algorithms for problems that can be formulated on graphs and networks. This edition provides many new applications and algorithms while maintaining the classic foundations on which contemporary algorithm...
Algorithms for Sparsity-Constrained Optimization (Springer Theses)
Algorithms for Sparsity-Constrained Optimization (Springer Theses)
This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a "greedy" algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use...
Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition
Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and...

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