Statistical datamining Using SAS Applications, Second Edition describes statistical datamining concepts and demonstrates the features of user.friendly datamining SAS tools. Integrating the statistical and graphical...
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ISBN:
(数字)9781439810767
ISBN:
(纸本)9781439810750
Statistical datamining Using SAS Applications, Second Edition describes statistical datamining concepts and demonstrates the features of user.friendly datamining SAS tools. Integrating the statistical and graphical analysis tools available in SAS systems, the book provides complete statistical datamining solutions without writing SAS program co
Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the me...
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ISBN:
(数字)9781420091502
ISBN:
(纸本)9781420091489
Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the methods and tools of privacy-preser.ing data publishing enable the publication of useful information while protecting data privacy. Int
The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive datamining tools. Exploring this area from the perspective of a practitioner, data Mini...
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ISBN:
(纸本)1439810184;9781439810187
The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive datamining tools. Exploring this area from the perspective of a practitioner, datamining with R: Learning with Case Studies uses practical examples to illustrate the power of R and datamining. Assuming no prior knowledge of R or datamining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main datamining processes and techniques, the author takes a hands-on approach that utilizes a ser.es of detailed, real-world case studies: Predicting algae blooms Predicting stock market returns Detecting fraudulent transactions Classifying microarray samples With these case studies, the author supplies all necessary steps, code, and data. Web ResourceA supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions.
A culmination of the authors' years of extensive research on this topic, Relational data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. ...
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ISBN:
(数字)9781420072624
ISBN:
(纸本)9781420072617
A culmination of the authors' years of extensive research on this topic, Relational data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems.
After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering:
Clustering on bi-type heterogeneous relational data Multi-type heterogeneous relational data Homogeneous relational data clustering Clustering on the most general case of relational data Individual relational clustering framework Recent research on evolutionary clustering
This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.
Exploiting the rich information found in electronic health records (EHRs) can facilitate better medical research and improve the quality of medical practice. Until now, a trivial amount of research has been published ...
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ISBN:
(数字)9781420090413
ISBN:
(纸本)9781420090383
Exploiting the rich information found in electronic health records (EHRs) can facilitate better medical research and improve the quality of medical practice. Until now, a trivial amount of research has been published on the challenges of leveraging this information. Addressing these challenges, Information discovery on Electronic Health Records exp
The Definitive Resource on Text mining Theory and Applications from Foremost Researchers in the FieldGiving a broad perspective of the field from numerous vantage points, Text mining: Classification, Clustering, and A...
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ISBN:
(数字)9781420059458
ISBN:
(纸本)9781420059403
The Definitive Resource on Text mining Theory and Applications from Foremost Researchers in the FieldGiving a broad perspective of the field from numerous vantage points, Text mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify te
The Best-Known Algorithms Currently Used in the datamining CommunityContributions from recognized leaders in the fieldIdentifying some of the most influential algorithms that are widely used in the datamining commun...
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ISBN:
(数字)9781420089653
ISBN:
(纸本)9781420089646
The Best-Known Algorithms Currently Used in the datamining CommunityContributions from recognized leaders in the fieldIdentifying some of the most influential algorithms that are widely used in the datamining community, The Top Ten Algorithms in datamining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective *** book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics—including classification, clustering, statistical learning, association analysis, and link mining—in datamining research and development as well as in datamining, machine learning, and artificial intelligence *** naming the leading algorithms in this field, this book encourages the use of datamining techniques in a broader realm of real-world applications. It should inspire more datamining researchers to further explore the impact and novel research issues of these algorithms.
Drawn from the US National Science Foundations Symposium on Next Generation of datamining and Cyber-Enabled discovery for Innovation (NGDM 07), Next Generation of datamining explores emerging technologies and applic...
ISBN:
(纸本)9781420085860
Drawn from the US National Science Foundations Symposium on Next Generation of datamining and Cyber-Enabled discovery for Innovation (NGDM 07), Next Generation of datamining explores emerging technologies and applications in datamining as well as potential challenges faced by the field. Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of datamining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance datamining. They also discuss the impact of new technologies, such as the semantic web, on datamining and provide recommendations for privacy-preser.ing mechanisms. The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for datamining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to datamining problems that span an array of disciplines.
Collecting the latest developments in the field, Multimedia datamining: A Systematic Introduction to Concepts and Theory defines multimedia datamining, its theory, and its applications. Two of the most active resear...
ISBN:
(纸本)9781584889663;1584889667
Collecting the latest developments in the field, Multimedia datamining: A Systematic Introduction to Concepts and Theory defines multimedia datamining, its theory, and its applications. Two of the most active researchers in multimedia datamining explore how this young area has rapidly developed in recent years. The book first discusses the theoretical foundations of multimedia datamining, presenting commonly used feature representation, knowledge representation, statistical learning, and soft computing techniques. It then provides application examples that showcase the great potential of multimedia datamining technologies. In this part, the authors show how to develop a semantic repository training method and a concept discovery method in an imagery database. They demonstrate how knowledgediscovery helps achieve the goal of imagery annotation. The authors also describe an effective solution to large-scale video search, along with an application of audio data classification and categorization. This novel, self-contained book examines how the merging of multimedia and datamining research can promote the understanding and advance the development of knowledgediscovery in multimedia data.
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