data mining applications with r

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Data Mining Applications With R

Author : Yanchang Zhao
ISBN : 9780124115200
Genre : Computers
File Size : 49. 81 MB
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Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool. R code, Data and color figures for the book are provided at the RDataMining.com website. Helps data miners to learn to use R in their specific area of work and see how R can apply in different industries Presents various case studies in real-world applications, which will help readers to apply the techniques in their work Provides code examples and sample data for readers to easily learn the techniques by running the code by themselves

R And Data Mining

Author : Yanchang Zhao
ISBN : 9780123972712
Genre : Mathematics
File Size : 21. 61 MB
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R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation. With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work

Data Mining Algorithms

Author : Pawel Cichosz
ISBN : 9781118332580
Genre : Computers
File Size : 67. 72 MB
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"This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective"--

Advanced Data Mining And Applications

Author : Reda Alhajj
ISBN : 9783540738701
Genre : Computers
File Size : 20. 15 MB
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This book constitutes the refereed proceedings of the Third International Conference on Advanced Data Mining and Applications, ADMA 2007, held in Harbin, China in August 2007. The papers focus on advancements in data mining and peculiarities and challenges of real world applications using data mining.

Database Support For Data Mining Applications

Author : Rosa Meo
ISBN : 3540224793
Genre : Computers
File Size : 37. 94 MB
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Data mining from traditional relational databases as well as from non-traditional ones such as semi-structured data, Web data, and scientific databases housing biological, linguistic, and sensor data has recently become a popular way of discovering hidden knowledge. This book on database support for data mining is developed to approaches exploiting the available database technology, declarative data mining, intelligent querying, and associated issues, such as optimization, indexing, query processing, languages, and constraints. Attention is also paid to the solution of data preprocessing problems, such as data cleaning, discretization, and sampling. The 16 reviewed full papers presented were carefully selected from various workshops and conferences to provide complete and competent coverage of the core issues. Some papers were developed within an EC funded project on discovering knowledge with inductive queries.

Classification Clustering And Data Mining Applications

Author : International Federation of Classification Societies. Conference
ISBN : 9783540220145
Genre : Computers
File Size : 31. 54 MB
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Modern data analysis stands at the interface of statistics, computer science, and discrete mathematics. This volume describes new methods in this area, with special emphasis on classification and cluster analysis. Those methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.

Advances On Data Mining Applications And Theoretical Aspects

Author : Petra Perner
ISBN : 9783642231834
Genre : Computers
File Size : 79. 28 MB
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This book constitutes the refereed proceedings of the 11th Industrial Conference on Data Mining, ICDM 2011, held in New York, USA in September 2011. The 22 revised full papers presented were carefully reviewed and selected from 100 submissions. The papers are organized in topical sections on data mining in medicine and agriculture, data mining in marketing, data mining for Industrial processes and in telecommunication, Multimedia Data Mining, theoretical aspects of data mining, Data Warehousing, WebMining and Information Mining.

Soft Computing For Data Mining Applications

Author : K. R. Venugopal
ISBN : 9783642001925
Genre : Computers
File Size : 24. 38 MB
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The authors have consolidated their research work in this volume titled Soft Computing for Data Mining Applications. The monograph gives an insight into the research in the ?elds of Data Mining in combination with Soft Computing methodologies. In these days, the data continues to grow - ponentially. Much of the data is implicitly or explicitly imprecise. Database discovery seeks to discover noteworthy, unrecognized associations between the data items in the existing database. The potential of discovery comes from the realization that alternate contexts may reveal additional valuable information. The rate at which the data is storedis growing at a phenomenal rate. Asaresult,traditionaladhocmixturesofstatisticaltechniquesanddata managementtools are no longer adequate for analyzing this vast collection of data. Severaldomainswherelargevolumesofdataarestoredincentralizedor distributeddatabasesincludesapplicationslikeinelectroniccommerce,bio- formatics, computer security, Web intelligence, intelligent learning database systems,?nance,marketing,healthcare,telecommunications,andother?elds. E?cient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the ca- bility of computers to search huge amounts of data in a fast and e?ective manner. However,the data to be analyzed is imprecise and a?icted with - certainty. In the case of heterogeneous data sources such as text and video, the data might moreover be ambiguous and partly con?icting. Besides, p- terns and relationships of interest are usually approximate. Thus, in order to make the information mining process more robust it requires tolerance toward imprecision, uncertainty and exceptions.

Data Mining For Scientific And Engineering Applications

Author : R.L. Grossman
ISBN : 9781461517337
Genre : Computers
File Size : 55. 51 MB
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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.

Advanced Data Mining And Applications

Author : Xue Li
ISBN : 9783540370253
Genre : Computers
File Size : 23. 2 MB
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Here are the proceedings of the 2nd International Conference on Advanced Data Mining and Applications, ADMA 2006, held in Xi'an, China, August 2006. The book presents 41 revised full papers and 74 revised short papers together with 4 invited papers. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, and more.

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