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Provides in-depth coverage of basic and advanced topics in data mining and knowledge discovery Presents the most popular data mining algorithms in an easy to follow format Includes instructional tutorials on applying the various data mining algorithms Provides several interesting datasets ready to be mined Offers in-depth coverage of RapidMiner Studio and Weka’s Explorer interface Teaches the reader (student,) hands-on, about data mining using RapidMiner Studio and Weka Gives instructors a wealth of helpful resources, including all RapidMiner processes used for the tutorials and for solving the end of chapter exercises. Instructors will be able to get off the starting block with minimal effort Extra resources include screenshot sequences for all RapidMiner and Weka tutorials and demonstrations, available for students and instructors alike The latest version of all freely available materials can also be downloaded at: http://krypton.mnsu.edu/~sa7379bt/
Just Enough R! An Interactive Approach to Machine Learning and Analytics presents just enough of the R language, machine learning algorithms, statistical methodology, and analytics for the reader to learn how to find interesting structure in data. The approach might be called "seeing then doing" as it first gives step-by-step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed by detailed scripts written in R that apply the algorithms to solve nontrivial problems with real data. The script code is provided, allowing the reader to execute the scripts as they study the explanations give...
Besides introducing the concepts and types of one of the most powerful and prevalent statistical techniques in experimental behavioral science research, this text also shows students how to perform analysis of variance using a calculator to provide a better feel for evaluating data than relying on an ANOVA computer program. Appendices include standard statistical tables and answers to chapter exercises.
This first comprehensive review of airborne measurement principles covers all atmospheric components and surface parameters. It describes the common techniques to characterize aerosol particles and cloud/precipitation elements, while also explaining radiation quantities and pertinent hyperspectral and active remote sensing measurement techniques along the way. As a result, the major principles of operation are introduced and exemplified using specific instruments, treating both classic and emerging measurement techniques. The two editors head an international community of eminent scientists, all of them accepted and experienced specialists in their field, who help readers to understand specific problems related to airborne research, such as immanent uncertainties and limitations. They also provide guidance on the suitability of instruments to measure certain parameters and to select the correct type of device. While primarily intended for climate, geophysical and atmospheric researchers, its relevance to solar system objects makes this work equally appealing to astronomers studying atmospheres of solar system bodies with telescopes and space probes.
In recent years, several new concepts have emerged in the field of stratospheric ozone depletion, creating a need for a concise in-depth publication covering the ozone-climate issue. This monograph fills that void in the literature and gives detailed treatment of recent advances in the field of stratospheric ozone depletion. It puts particular emphasis on the coupling between changes in the ozone layer and atmospheric change caused by a changing climate. The book, written by leading experts in the field, brings the reader the most recent research in this area and fills the gap between advanced textbooks and assessments.
Annotation Rodgers (U. of Oxford) provides graduate students and other researchers a background to the inverse problem and its solution, with applications relating to atmospheric measurements. He introduces the stages in the reverse order than the usual approach in order to develop the learner's intuition about the nature of the inverse problem. Annotation copyrighted by Book News, Inc., Portland, OR.
in a near-future world where most of New York is under water and the mainland US is a bastion of censorship and religious extremism, PI Simone Pierce plies her trade between the tops of skyscrapers and over the networks of bridges. A routine case helping an archaeologist search for artworks lost when the seas rose turns deadly as bodies start floating to the surface of the Manhattan waters.
Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.
First published in 1796, Camilla, Fanny Burney's third novel, proved to be an enormous popular success. It deals with the matrimonial concerns of a group of young people-Camilla Tyrold and her sisters, the daughters of a country parson, and their cousin Indiana Lynmere-and, in particular, with the love affair between Camilla herself and her eligible suitor, Edgar Mandlebert.
Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. Th...