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• Sales Rank: #2986098 in Books
• Published on: 2012-07-07
• Original language: English
• Dimensions: 11.69" h x .32" w x 8.26" l,
• Binding: Paperback
• 138 pages

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Introducing the Selected Works of Simone Weil

• Sales Rank: #220042 in Books
• Published on: 2015-12-22
• Released on: 2015-12-22
• Original language: English
• Number of items: 1
• Dimensions: 8.50" h x .87" w x 5.50" l, .99 pounds
• Binding: Paperback
• 386 pages

Language Notes
Text: English, French (translation)

About the Author
Simone Weil (1909-1943) was one of the twentieth century's most profound thinkers. In her early years, she was known for her brilliant and biting social commentary, and especially for the year she spent working in three Paris factories. After three profound religious experiences, she did not abandon her work on social problems, but also began to write truly original and penetrating religious and philosophical works that still bear on our times, writings that were only published after her death.

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The analysis of gene expression profile data from DNA micorarray studies are discussed in this book. It provides a review of available methods and presents it in a manner that is intelligible to biologists. It offers an understanding of the design and analysis of experiments utilizing microarrays to benefit scientists. It includes an Appendix tutorial on the use of BRB-ArrayTools and step by step analyses of several major datasets using this software which is available from the National Cancer Institute.

• Sales Rank: #1509282 in Books
• Published on: 2004-01-08
• Ingredients: Example Ingredients
• Original language: English
• Number of items: 1
• Dimensions: 9.20" h x .80" w x 6.10" l, 1.30 pounds
• Binding: Hardcover
• 200 pages

Review

From the reviews:

"This book gives a welcome introduction to microarray data and its analysis. Such analyses have been performed for many years, but only recently are new investigators able to read about the topic in such a concise and well-written format." Journal of Biopharmaceutical Statistics, Issue #4, 2005

"I enjoyed it and recommend it for anyone working in this area." Short Book Reviews of the ISI, April 2005

"This is a book to get your biological collaborators to read, because it provides good statistical perspectives on the design of experiments and on other methodologies needed to design and analyze microarray experiments. The treatment is straightforward, but not simple." Journal of the American Statistical Association, June 2005

"The book presents the basics of microarray technology and follows with chapters on designing experiments and processing the scanned images. Further to this, it provides an introduction to selecting appropriate analysis techniques that will result in valid answers. … The understanding of many of the statistical applications is enhanced by the inclusion of several real-world medical datasets as examples." (Rebecca E. Walls)

"A very good overview of the issues and techniques, suitable for a non-specialist readership that would be interested to embark on microarray studies. Throughout, the authors discuss both spotted cDNA arrays and oligonucleotide arrays … which adds further merit to this book. … A great asset of this book is that all the example data are freely available for analysis … . an excellent introduction to all aspects of microarray analyses … a very good read … ." (Dirk – Jan De Koning, Genetical Research, Vol. 84, 2004)

"This book presents a concise overview of many useful statistical approaches to microarray data … . This broad range of topics makes the book unique and useful. … this book is a valuable resource for microarray experimenters. I enjoyed it and recommend it for anyone working in this area." (R. Tibshirani, Short Book Reviews, Vol. 25(1), 2005)

"This book is concerned with methods of analyzing data obtained from DNA microarray experiments. … The book is written for biologists without a strong statistical background, and also for computer scientists and statisticians interested in being collaborators in studies utilizing microarrays." (Alexander Mitrophanov, Zentralblatt MATH, Vol. 1039(8), 2004)

 "The small number of noisy microarrays relative to the large number of genes presents a challenge to biologists using this technology. This book aims to aid the biologist in solving some of these problems … . It largely achieves this aim. … the chapters are logically integrated and perfectly consistent with each other. … describes, overall, a sensible approach to microarray analysis. … the authors have done an excellent job in presenting sensible statistical methodology to the biological community." (Ernst Wit, Human Genetics, Vol. 1(6), November

"For the biomedical researcher, it promises an introduction and survey to the principles of biometrical thinking … . For the biometrician, it describes typical experimental approaches, data structures, design and analysis problems … . We are convinced that this book is a must for all readers who are interested in the design, conduct and analysis of microarray gene expression experiments. It will certainly impact on the necessary communication between researchers of all type engaged in this truly cross disciplinary subject." (Dirk Repsilber and Andreas Ziegler, Metrika, Vol. 64, 2006)

"Analysis of data arising from microarray experiments is one of the very exciting areas in statistics these days. … it is important to have text books that can serve the dual purpose of catering basic biology related to microarray experiments to statisticians and exposing to biologists the basic statistical techniques needed for designing microarray experiments and analyzing data generated from such experiments. … This book will be quite useful for biostatisticians and biologists interested in microarray experiments and related data analysis." (Probal Chaudhuri, Sankhya, Vol. 67 (1), 2005)

"Microarray experiments are rapidly growing in popularity for their ability to analyze DNA samples with relative ease. This book, penned by a collection of current and former members of the Biometric Research Branch at the National Cancer Institute, offers a concise yet comprehensive guide to designing and analyzing such experiments. … The book is aimed at biologists with limited statistical knowledge … ." (Journal of Applied Statistics, Vol. 32 (4), 2005)

From the Back Cover

This book is targeted to biologists with limited statistical background and to statisticians and computer scientists interested in being effective collaborators on multi-disciplinary DNA microarray projects. State-of-the-art analysis methods are presented with minimal mathematical notation and a focus on concepts. This book is unique because it is authored by statisticians at the National Cancer Institute who are actively involved in the application of microarray technology.

Many laboratories are not equipped to effectively design and analyze studies that take advantage of the promise of microarrays. Many of the software packages available to biologists were developed without involvement of statisticians experienced in such studies and contain tools that may not be optimal for particular applications. This book provides a sound preparation for designing microarray studies that have clear objectives, and for selecting analysis tools and strategies that provide clear and valid answers. The book offers an in depth understanding of the design and analysis of experiments utilizing microarrays and should benefit scientists regardless of what software packages they prefer. In order to provide all readers with hands on experience in data analysis, it includes an Appendix tutorial on the use of BRB-ArrayTools and step by step analyses of several major datasets using this software which is freely available from the National Cancer Institute for non-commercial use.

The authors are current or former members of the Biometric Research Branch at the National Cancer Institute.  They have collaborated on major biomedical studies utilizing microarrays and in the development of statistical methodology for the design and analysis of microarray investigations. Dr. Simon, chief of the branch, is also the architect of BRB-ArrayTools.

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0 of 3 people found the following review helpful.
By now out of date.
By Dirk Dittmer
By now out of date.

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• Sales Rank: #6451778 in Books
• Published on: 2011-11-01
• Binding: Single Issue Magazine

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• Sales Rank: #5413310 in Books
• Published on: 1946
• Binding: Hardcover
• 3 pages

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0 of 0 people found the following review helpful.
WWII Naval Aviation at it's best!
By Robert H. Scudder
VF-31 which is the center of this well-written book continued on for many years and in fact became the last Navy fighter squadron to fly the venerable F-14 "Tomcat"

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• Sales Rank: #8821480 in Books
• Published on: 2003
• Original language: English
• Dimensions: 1.10" h x 10.70" w x 13.20" l, 4.93 pounds
• Binding: Hardcover

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3 of 3 people found the following review helpful.
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By Mark Fair
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Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

• Sales Rank: #31361 in Books
• Brand: Tan, Pang-Ning/ Steinbach, Michael/ Kumar, Vipin
• Published on: 2005-05-12
• Original language: English
• Number of items: 1
• Dimensions: 9.40" h x 1.60" w x 7.40" l, 2.60 pounds
• Binding: Hardcover
• 769 pages

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78 of 79 people found the following review helpful.
Data mining book focusing on clustering
By Sandro Saitta
I decided to start with this book as I think it is the most convenient to start in the data mining field. One big advantage of the book is the way data mining techniques are explained. It is mainly based on textual and graphical explanations. There is little equations, only what is necessary to implement the algorithms.

This book widely cover areas such as data preparation and understanding, classification, anomaly detection, association analysis and clusering. Although the book has a strong emphasis on the two last ones, nearly all standard data mining techniques are at least briefly discussed. However, this book does only have a fiew pages about kernel methods for example. Indeed, it is normal, as kernel methods are more suitable for machine learning (I mean making prediction) than data mining (I mean looking for description).

Therefore, this book is:

* able to explain data mining without thousands of equations

* a good way to start with data mining

* covering nearly all standard data mining techniques

* focused on association analysis and clustering

and it is not:

* a good book for kernel methods and other advanced techniques

* written in the statistical nor in the database perspective

My comment: if you are in the data mining field and not comming from mathematics or databases, then you really should buy this book.

56 of 59 people found the following review helpful.
superb description of clusters
By W Boudville
As databases keep growing unabatedly, so too has the need for smart data mining. For a competitive edge in business, it helps to be able to analyse your data in unique ways. This text gives you a thorough education in state of the art data mining. Appropriate for both a student and a professional in the field.

The extensive problem sets are well suited for the student. These often expand on concepts in the narrative, and are worth tackling.

The central theme in the book is how to classify data, or find associations or clusters within it.

Cluster analysis gets two chapters that are superbly done. These summarise decades of research into methods of grouping data into clusters. Usually hard to do, because an element of subjectivity can creep into the results. If your data is scattered in some n-dimensional space, then clusters might exist. But how to find them? The chapters show that the number of clusters and the constituents of these can depend on which method you adopt, and various initial conditions, like [essentially] seed values for clusters, if you choose a prototype cluster method like K-means.

The descriptions of the cluster algorithms are succinct. Why is this useful? Because it helps you easily understand the operations of the algorithms, without drowning you in low level detail. Plus, by presenting a meta-level comparison between the algorithms, you can develop insight into rolling your own methods, specific to your data.

Part of my research involves finding new ways to make clusters, and the text was very useful in explaining the existing ideas.

41 of 45 people found the following review helpful.
Good overview, but needs to include real-world case studies
By Dr. Lee D. Carlson
Data mining could be considered to be "Artificial Intelligence Lite", since it deals with many of the same issues in learning, classification, and analysis as they occur in the field of artificial intelligence but does not have as its goal the construction of "thinking machines." Instead, the emphasis is on practical problems that are important in business and industry, even though the solutions of many of these problems makes use of techniques that a thinking machine should be expected to have. Data mining has become an enormous industry, and has even been the subject of political and legal concerns due to the efforts of some governments to mine data on its citizens. This book gives a general overview of data mining with emphasis on classification and associative analysis. Anyone who is interested in data mining could read the book, but some rather sophisticated background in mathematics will be needed to read some of the sections. Pseudocode is given throughout the book to illustrate the different data mining algorithms. There are also exercises at the end of each chapter, but noticeably missing in the book is the inclusion of real case studies in data mining. The inclusion of these case studies would alert the reader to the fact that data mining is of great interest from the standpoint of business and industry, and would lessen the belief that data mining is just another academic field or just another branch of statistics.

Speaking somewhat loosely, the goal of data mining is to find interesting patterns in massive amounts of data or the classification of such patterns. This entails of course that one have a notion of what is "interesting" and one of the main problems in data mining is to find suitable `interestingness measures'. And since one is typically dealing with large amounts of data, one must use various statistical sampling and preprocessing techniques to massage the data and obtain a `representative' sample of the original data. In addition, one must be able to handle data that is `anomalous', i.e. data that has characteristics that are markedly different from most of the other data, or that has attributes that are unusual if compared with typical values for those attributes. These issues and techniques are discussed in detail in the first three chapters of the book, where the authors outline some of the bread-and-butter topics needed for effective manipulation of data.

The real substance and power of data mining comes from its role in classification and for discovering interesting patterns in huge data sets. The authors, in chapters 4 - 7, discuss various powerful techniques for data classification and association analysis. Association analysis in particular has been used quite extensively in recent years, due to the use of market basket transactions in on-line purchasing and the goal of marketers to learn the purchasing behavior of their customers. Association analysis uncovers relationships in the marketing data in the form of `association rules'. For disjoint itemsets X and Y, an association rule is a logical implication expression between these itemsets that has a certain `strength' that is measured by its `support' and `confidence.' The support measures how often a rule is applicable to a given data set, while the confidence measures how frequently the items in Y appear in X. The support reflects the ability of the rule to be not due to chance alone, while the confidence measures the reliability of the rule inference. The collection of all association rules that can be formed from a data set is too large to be practical and so strategies must be developed to prune the number of rules. The authors discuss in detail various methods for dealing with this computational drawback, such as `frequent itemset generation' and `rule generation.'

The detection of anomalies consists of the identification of `outliers', which as the name implies are data objects that lie "far away" from the other data objects. It remains of course to quantity what it means to be "far away" and for this reason this branch of data mining, as the author points out, is sometimes called `deviation detection' or `exception mining'. The omission of outliers is sometimes justified, since they are merely artifacts that only serve to alter the statistics of a particular data set. However, sometimes their presence signals important information, if not a major scientific discovery. Data mining therefore must contain tools that detect anomalies intelligently and efficiently. The authors discuss anomaly detection in fair detail, emphasizing the statistical techniques that are available to do it. They classify the techniques for anomaly detection as being `unsupervised', `supervised', and `semi-supervised'. As the name implies, supervised anomaly detection requires the existence of a training set with both anomalous and "normal" data with each class being labeled as such. When these labels are unavailable, one has to perform unsupervised anomaly detection, and for this approach to work the anomalies must be distinct from one another. If the normal data is labeled but the anomalies are not, one must do semi-supervised anomaly detection. The only weakness in the authors' discussion is that they do not include real-world case studies that illustrate the different techniques, such as clustering and density methods.

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• Sales Rank: #10463742 in Books
• Published on: 2008-09-30
• Original language: English
• Number of items: 1
• Dimensions: 9.30" h x .80" w x 6.10" l, 1.50 pounds
• Binding: Hardcover
• 339 pages

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This concise book is a quick start-up primer on the Gradle build and release tool. You'll learn about Gradle and how to set it up to use. You'll learn about the Gradle life cycle and how to effectively leverage and use it. Also, you'll learn the basics of using site plugins and generating Javadocs, test coverage/FindBugs reports, version/release notes and more. Furthermore, you'll take advantage of Gradle's archetypes to bootstrap new projects easily.

After reading and using Introducing Gradle, you'll have an understanding of Gradle's dependency management and how to organize basic and multi-module Gradle projects. It includes the new features and enhancements found in the latest version of Gradle, an open source enhanced Java build and release tool for Java that is especially popular among Spring application developers.

• Sales Rank: #3308105 in Books
• Published on: 2015-12-09
• Released on: 2015-12-23
• Original language: English
• Number of items: 1
• Dimensions: 9.25" h x .35" w x 6.10" l, .0 pounds
• Binding: Paperback
• 130 pages

About the Author
Balaji Varanasi is a software development manager and technology entrepreneur. He has over 13 years of experience architecting and developing Java/.Net applications and, more recently, iPhone apps. During this period he has worked in the areas of security, web accessibility, search, and enterprise portals. He has a Master s Degree in Computer Science and serves as adjunct faculty, teaching programming and information system courses. When not programming, he enjoys spending time with his lovely wife in Salt Lake City, Utah.

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