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Statistical Data Mining Using SAS Applications, Second Edition

Statistical Data Mining Using SAS Applications, Second Edition describes statistical data mining concepts and demonstrates the features of user-friendly data mining SAS tools. Integrating the statistical and graphical analysis tools available in SAS systems, the book provides complete statistical data mining solutions without writing SAS program codes or using the point-and-click approach. Each chapter emphasizes step-by-step instructions for using SAS macros and interpreting the results. Compiled data mining SAS macro files are available for download on the author’s website. By following the step-by-step instructions and downloading the SAS macros, analysts can perform complete data mining analysis fast and effectively. New to the Second Edition—General Features Access to SAS macros directly from desktop Compatible with SAS version 9, SAS Enterprise Guide, and SAS Learning Edition Reorganization of all help files to an appendix Ability to create publication quality graphics Macro-call error check New Features in These SAS-Specific Macro Applications Converting PC data files to SAS data (EXLSAS2 macro) Randomly splitting data (RANSPLIT2) Frequency analysis (FREQ2) Univariate analysis (UNIVAR2) PCA and factor analysis (FACTOR2) Multiple linear regressions (REGDIAG2) Logistic regression (LOGIST2) CHAID analysis (CHAID2) Requiring no experience with SAS programming, this resource supplies instructions and tools for quickly performing exploratory statistical methods, regression analysis, logistic regression multivariate methods, and classification analysis. It presents an accessible, SAS macro-oriented approach while offering comprehensive data mining solutions.

Data mining, or knowledge discovery in databases (KDD), is a powerful
information technology tool with great potential for extracting previously unknown
and potentially useful information from large databases. Data mining automates
the ...

Applications of Data Mining in E-business and Finance

The application of Data Mining (DM) technologies has shown an explosive growth in an increasing number of different areas of business, government and science. Two of the most important business areas are finance, in particular in banks and insurance companies, and e-business, such as web portals, e-commerce and ad management services.In spite of the close relationship between research and practice in Data Mining, it is not easy to find information on some of the most important issues involved in real world application of DM technology, from business and data understanding to evaluation and deployment. Papers often describe research that was developed without taking into account constraints imposed by the motivating application. When these issues are taken into account, they are frequently not discussed in detail because the paper must focus on the method. Therefore knowledge that could be useful for those who would like to apply the same approach on a related problem is not shared. The papers in this book address some of these issues. This book is of interest not only to Data Mining researchers and practitioners, but also to students who wish to have an idea of the practical issues involved in Data Mining.

All rights reserved. doi:10.3233/978-1-58603-890-8-133 Sequence Mining for
Business Analytics: Building Project Taxonomies for Resource Demand
Forecasting Ritendra DATTA a, Jianying HU b and Bonnie RAY b,c aDepartment
of ...

Agents and Data Mining Interaction

9th International Workshop, ADMI 2013, Saint Paul, MN, USA, May 6-7, 2013, Revised Selected Papers

This book constitutes the thoroughly refereed and revised selected papers from the 9th International Workshop on Agents and Data Mining Interaction, ADMI 2013, held in Saint Paul, MN, USA in May 2013. The 10 papers presented in this volume were carefully selected for inclusion in the book and are organized in topical sections named agent mining and data mining.

We are pleased to welcome you to the proceedings of the 2013 International
Workshop on Agents and Data Mining Interaction (ADMI-13), held jointly with
AAMAS 2013. In recent years, agents and data mining interaction (ADMI, or
agent ...

Association Rule Hiding for Data Mining

Privacy and security risks arising from the application of different data mining techniques to large institutional data repositories have been solely investigated by a new research domain, the so-called privacy preserving data mining. Association rule hiding is a new technique in data mining, which studies the problem of hiding sensitive association rules from within the data. Association Rule Hiding for Data Mining addresses the problem of "hiding" sensitive association rules, and introduces a number of heuristic solutions. Exact solutions of increased time complexity that have been proposed recently are presented, as well as a number of computationally efficient (parallel) approaches that alleviate time complexity problems, along with a thorough discussion regarding closely related problems (inverse frequent item set mining, data reconstruction approaches, etc.). Unsolved problems, future directions and specific examples are provided throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problem. Association Rule Hiding for Data Mining is designed for researchers, professors and advanced-level students in computer science studying privacy preserving data mining, association rule mining, and data mining. This book is also suitable for practitioners working in this industry.

Association Rule Hiding for Data Mining addresses the optimization problem of “hiding” sensitive association rules which due to its combinatorial nature admits a number of heuristic solutions that will be proposed and presented in this ...

Data Mining: Foundations and Intelligent Paradigms

VOLUME 2: Statistical, Bayesian, Time Series and other Theoretical Aspects

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

Chapter 4 A Visual Environment for Designing and Running Data Mining
Workflows in the Knowledge Grid Eugenio Cesario1, Marco Lackovic2,
Domenico Talia1,2, and Paolo Trunfio2 1 ICAR-CNR 2 DEIS, University of
Calabria Via P. Bucci ...

Data Mining

Concepts, Models, Methods, and Algorithms

This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.

Therefore, building a data-mining model is not a straightforward procedure, but a
very sensitive process requiring, in many cases, feedback information for multiple
mining iterations. In the final phase of the data-mining process, when the ...

Java Data Mining: Strategy, Standard, and Practice

A Practical Guide for Architecture, Design, and Implementation

Whether you are a software developer, systems architect, data analyst, or business analyst, if you want to take advantage of data mining in the development of advanced analytic applications, Java Data Mining, JDM, the new standard now implemented in core DBMS and data mining/analysis software, is a key solution component. This book is the essential guide to the usage of the JDM standard interface, written by contributors to the JDM standard. Data mining introduction - an overview of data mining and the problems it can address across industries; JDM's place in strategic solutions to data mining-related problems JDM essentials - concepts, design approach and design issues, with detailed code examples in Java; a Web Services interface to enable JDM functionality in an SOA environment; and illustration of JDM XML Schema for JDM objects JDM in practice - the use of JDM from vendor implementations and approaches to customer applications, integration, and usage; impact of data mining on IT infrastructure; a how-to guide for building applications that use the JDM API Free, downloadable KJDM source code referenced in the book available here

Data mining is becoming a mainstream technology used in business intelligence
applications supporting industries such as financial services, retail, healthcare,
telecommunications, and higher education, and lines of business such as ...

Data Mining

A Knowledge Discovery Approach

This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.

Data Mining for Association Rules and Sequential Patterns

Sequential and Parallel Algorithms

The book provides a unified presentation of algorithms for association rule and sequential pattern discovery. For both mining problems, the presentation relies on the lattice structure of the search space. All algorithms are built as processes running on this structure. Proving their properties takes advantage of the mathematical properties of the structure. Mining for association rules and sequential patterns is known to be a problem with large computational complexity. The issue of designing efficient parallel algorithms should be considered as critical. Most algorithms in the book are devised for both sequential and parallel execution. Parallel algorithm design takes advantage of the lattice structure of the search space. Partitioning is performed via lattice recursive bisection. Database partitioning is also used as an additional source of parallelism. Part of the motivation for writing this book was postgraduate teaching. Since the book only assumes elementary mathematical knowledge in the domains of lattices, combinatorial optimization, probability calculus, and statistics, it is fit for use by undergraduate students as well. The algorithms are described in a C-like pseudo programming language. The computations are shown in great detail. This makes the book also fit for use by implementers: computer scientists in many domains as well as industry engineers.

In this chapter, we give a formulation of the problem of mining for sequential
patterns in a Boolean database. Next, we devise sequential and parallel
algorithms, based on search space partitioning, that solve this problem. In
Section 10.1, we ...

High Performance Data Mining

Scaling Algorithms, Applications and Systems

High Performance Data Mining: Scaling Algorithms, Applications and Systems brings together in one place important contributions and up-to-date research results in this fast moving area. High Performance Data Mining: Scaling Algorithms, Applications and Systems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

High Performance Data Mining: Scaling Algorithms, Applications and Systems brings together in one place important contributions and up-to-date research results in this fast moving area.