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Data Warehousing Olap And Data Mining

Data Mining Applications with R

Data Mining Methods for the Content Analyst

An Introduction to the Computational Analysis of Content

Data Mining on Multimedia Data

Statistics, Data Mining, and Machine Learning in Astronomy

A Practical Python Guide for the Analysis of Survey Data

Data Mining Patterns: New Methods and Applications

Data Mining and Data Analysis for Counterterrorism

Defeating terrorism requires a nimble intelligence apparatus that operates actively within the United States and makes use of advanced information technology. This report makes practical recommendatio
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Data Warehousing and Data Mining Techniques for Cyber Security

Data Mining and Data Visualization

Multimedia Data Mining and Knowledge Discovery

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Data Warehousing Olap And Data Mining

This Book Is Mainly Intended For It Students And Professionals To Learn Or Implement Data Warehousing Technologies. It Experiences The Real-Time Environment And Promotes Planning, Managing, Designing, Implementing, Supporting, Maintaining And Analyzing Data Warehouse In Organizations And It Also Provides Various Mining Techniques As Well As Issues In Practical Use Of Data Mining Tools.The Book Is Designed For The Target Audience Such As Specialists, Trainers And It Users. It Does Not Assume Any Special Knowledge As Background. Understanding Of Computer Use, Databases And Statistics Will Be Helpful.

S. Nagabhushana,

Introduction 249 1.1 What is Data Mining 251 1.2 Definitions 252 1.3 Data Mining
Process 253 1.4 Data Mining Background 254 1.5 Data Mining Models 256 1.6
Data Mining Methods 257 1.7 Data Mining Problems/Issues 260 1.8 Potential ...

Data Mining Applications with R

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

Yanchang Zhao, Yonghua Cen,

This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas.

Web Data Mining

Exploring Hyperlinks, Contents, and Usage Data

Web mining aims to discover useful information and knowledge from Web hyperlinks, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semi-structured and unstructured nature of the Web data. The field has also developed many of its own algorithms and techniques. 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.

Bing Liu,

Web mining aims to discover useful information or knowledge from Web
hyperlinks, page contents, and usage logs. Based on the primary kinds of data
used in the mining process, Web mining tasks can be categorized into three main
types: ...

Data Mining Methods for the Content Analyst

An Introduction to the Computational Analysis of Content

With continuous advancements and an increase in user popularity, data mining technologies serve as an invaluable resource for researchers across a wide range of disciplines in the humanities and social sciences. In this comprehensive guide, author and research scientist Kalev Leetaru introduces the approaches, strategies, and methodologies of current data mining techniques, offering insights for new and experienced users alike. Designed as an instructive reference to computer-based analysis approaches, each chapter of this resource explains a set of core concepts and analytical data mining strategies, along with detailed examples and steps relating to current data mining practices. Every technique is considered with regard to context, theory of operation and methodological concerns, and focuses on the capabilities and strengths relating to these technologies. In addressing critical methodologies and approaches to automated analytical techniques, this work provides an essential overview to a broad innovative field.

Kalev Leetaru,

In this comprehensive guide, author and research scientist Kalev Leetaru introduces the approaches, strategies, and methodologies of current data mining techniques, offering insights for new and experienced users alike.

Data Mining on Multimedia Data

Despite being a young field of research and development, data mining has proved to be a successful approach to extracting knowledge from huge collections of structured digital data collection as usually stored in databases. Whereas data mining was done in early days primarily on numerical data, nowadays multimedia and Internet applications drive the need to develop data mining methods and techniques that can work on all kinds of data such as documents, images, and signals. This book introduces the basic concepts of mining multimedia data and demonstrates how to apply these methods in various application fields. It is written for students, ambitioned professionals from industry and medicine, and for scientists who want to contribute R&D work to the field or apply this new technology.

Petra Perner,

In this book we have given an overview about data mining for multimedia data.
The book does not describe all aspects of data mining. We have focused on
methods which are from our point of view most important for mining multimedia
data.

Principles of Data Mining

The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

D. J. Hand, Heikki Mannila, Padhraic Smyth, David J. Hand, Heikki Mannila, Padhraic Smyth, Heikki Mannila, Padhraic Smyth,

This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections.

Statistics, Data Mining, and Machine Learning in Astronomy

A Practical Python Guide for the Analysis of Survey Data

As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers

Željko Ivezić, Andrew J. Connolly, Jacob T VanderPlas, Alexander Gray,

This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response ...

Data Mining Patterns: New Methods and Applications

New Methods and Applications

"This book provides an overall view of recent solutions for mining, and explores new patterns,offering theoretical frameworks and presenting challenges and possible solutions concerning pattern extractions, emphasizing research techniques and real-world applications. It portrays research applications in data models, methodologies for mining patterns, multi-relational and multidimensional pattern mining, fuzzy data mining, data streaming and incremental mining"--Provided by publisher.

Poncelet, Pascal,

Data Mining and Data Analysis for Counterterrorism

Mary DeRosa,

Data Warehousing and Data Mining Techniques for Cyber Security

The application of data warehousing and data mining techniques to computer security is an important emerging area, as information processing and internet accessibility costs decline and more and more organizations become vulnerable to cyber attacks. These security breaches include attacks on single computers, computer networks, wireless networks, databases, or authentication compromises. This book describes data warehousing and data mining techniques that can be used to detect attacks. It is designed to be a useful handbook for practitioners and researchers in industry, and is also suitable as a text for advanced-level students in computer science.

Anoop Singhal,

6.2 Research Issues in Data Mining In this section, we briefly discuss some of the
research issues in data mining. a) Mining methodology and user interaction
issues: • Data mining query languages • Presentation and visualization of data ...

Data Mining and Data Visualization

Data Mining and Data Visualization focuses on dealing with large-scale data, a field commonly referred to as data mining. The book is divided into three sections. The first deals with an introduction to statistical aspects of data mining and machine learning and includes applications to text analysis, computer intrusion detection, and hiding of information in digital files. The second section focuses on a variety of statistical methodologies that have proven to be effective in data mining applications. These include clustering, classification, multivariate density estimation, tree-based methods, pattern recognition, outlier detection, genetic algorithms, and dimensionality reduction. The third section focuses on data visualization and covers issues of visualization of high-dimensional data, novel graphical techniques with a focus on human factors, interactive graphics, and data visualization using virtual reality. This book represents a thorough cross section of internationally renowned thinkers who are inventing methods for dealing with a new data paradigm. Distinguished contributors who are international experts in aspects of data mining Includes data mining approaches to non-numerical data mining including text data, Internet traffic data, and geographic data Highly topical discussions reflecting current thinking on contemporary technical issues, e.g. streaming data Discusses taxonomy of dataset sizes, computational complexity, and scalability usually ignored in most discussions Thorough discussion of data visualization issues blending statistical, human factors, and computational insights

DOI 10.1016/S0169-7161(04)24002-0 From Data Mining to Knowledge Mining
Kenneth A. Kaufman and Ryszard S. Michalski Abstract In view of the
tremendous production of computer data worldwide, there is a strong need for
new powerful ...

Multimedia Data Mining and Knowledge Discovery

This volume provides an overview of multimedia data mining and knowledge discovery and discusses the variety of hot topics in multimedia data mining research. It describes the objectives and current tendencies in multimedia data mining research and their applications. Each part contains an overview of its chapters and leads the reader with a structured approach through the diverse subjects in the field.

Valery A. Petrushin, Latifur Khan,

This volume provides an overview of multimedia data mining and knowledge discovery and discusses the variety of hot topics in multimedia data mining research.

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