Sebanyak 321 item atau buku ditemukan

Love Your Job

The New Rules for Career Happiness

A New York Times columnist and AARP's Jobs Expert describes how to turn your daily grind back into your dream job through developing new habits that give purpose to your workday, recognizing and changing negative patterns and celebrating small successes. Original.

Kerry Hannon leads you to that place. Don't miss this book!" —Richard Leider, international bestselling author of The Power of Purpose, Repacking Your Bags, and Life Reimagined "Love Your Job is a great addition to the AARP catalog.

Data mining solutions

methods and tools for solving real-world problems

Cutting-edge data mining techniques and tools for solving your toughest analytical problems Data Mining Solutions In down-to-earth language, data mining experts Christopher Westphal and Teresa Blaxton introduce a brand new approach to data mining analysis. Through their extensive real-world experience, they have developed and documented many practical and proven techniques to make your own data mining efforts more successful. You'll get a refreshing "out-of-the-box" approach to data mining that will help you maximize your time and problem-solving resources, and prepare for the next wave of data mining-visualization. You will read about ways in which data mining has been used to: * Discover patterns of insider trading in the stock market * Evaluate the utility of marketing campaigns * Analyze retail sales patterns across geographic regions * Identify money laundering operations * Target DNA sequences for pharmaceutical testing and development The book is accompanied by a CD-ROM that contains: * Demo and trial versions of numerous visual data mining tools * Active web-page links for each of the products profiled * GIF files corresponding to all book images

Unfortunately the field of data mining will be no different. Already we are
bombarded with a host of new buzzwords that sound great, but in reality are old
ideas being touted as part of the new data mining technology. Let us state from
the outset ...

Data Mining with SQL Server 2005

Your in–depth guide to using the new Microsoft data mining standard to solve today′s business problems Concealed inside your data warehouse and data marts is a wealth of valuable information just waiting to be discovered. All you need are the right tools to extract that information and put it to use. Serving as your expert guide, this book shows you how to create and implement data mining applications that will find the hidden patterns from your historical datasets. The authors explore the core concepts of data mining as well as the latest trends. They then reveal the best practices in the field, utilizing the innovative features of SQL Server 2005 so that you can begin building your own successful data mining projects. You′ll learn: The principal concepts of data mining How to work with the data mining algorithms included in SQL Server data mining How to use DMX–the data mining query language The XML for Analysis API The architecture of the SQL Server 2005 data mining component How to extend the SQL Server 2005 data mining platform by plugging in your own algorithms How to implement a data mining project using SQL Server Integration Services How to mine an OLAP cube How to build an online retail site with cross–selling features How to access SQL Server 2005 data mining features programmatically

Serving as your expert guide, this book shows you how to create and implement data mining applications that will find the hidden patterns from your historical datasets.

Data Mining Cookbook

Modeling Data for Marketing, Risk, and Customer Relationship Management

Increase profits and reduce costs by utilizing this collection of models of the most commonly asked data mining questions In order to find new ways to improve customer sales and support, and as well as manage risk, business managers must be able to mine company databases. This book provides a step-by-step guide to creating and implementing models of the most commonly asked data mining questions. Readers will learn how to prepare data to mine, and develop accurate data mining questions. The author, who has over ten years of data mining experience, also provides actual tested models of specific data mining questions for marketing, sales, customer service and retention, and risk management. A CD-ROM, sold separately, provides these models for reader use.

Increase profits and reduce costs by utilizing this collection of models of the most commonly asked data mining questions In order to find new ways to improve customer sales and support, and as well as manage risk, business managers must be ...

Data Mining Methods & Models

The book introduces readers to data mining methods and models, including association rules, clustering, K-nearest neighbor, statistical inference, neural networks, linear and logistic regression, and multivariate analysis. Taking a unified approach based on CRISP methodology, the book discusses the latest techniques for uncovering hidden nuggets of information and provides insight into how the data mining algorithms actually work with hands-on experience performing data mining on large data sets. · Dimension Reduction Methods · Regression Modeling · Multiple Regression and Model Building · Logistic Regression · Naïve Bayes and Bayesian Networks · Genetic Algorithms · Case Study: Modeling Response to Direct-Mail Marketing

The book introduces readers to data mining methods and models, including association rules, clustering, K-nearest neighbor, statistical inference, neural networks, linear and logistic regression, and multivariate analysis.

Data Mining for Business Intelligence

Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner

Learn how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business Intelligence In today's world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success. Data Mining for Business Intelligence, which was developed from a course taught at the Massachusetts Institute of Technology's Sloan School of Management, and the University of Maryland's Smith School of Business, uses real data and actual cases to illustrate the applicability of data mining intelligence to the development of successful business models. Featuring XLMiner, the Microsoft Office Excel add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of data mining techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples are provided to motivate learning and understanding. Data Mining for Business Intelligence: Provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis Features a business decision-making context for these key methods Illustrates the application and interpretation of these methods using real business cases and data This book helps readers understand the beneficial relationship that can be established between data mining and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiser business decisions.

Data Mining for Business Intelligence: Provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis Features a business decision-making context for ...

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 ...

Data Mining in Grid Computing Environments

Based around eleven international real life case studies and including contributions from leading experts in the field this groundbreaking book explores the need for the grid-enabling of data mining applications and provides a comprehensive study of the technology, techniques and management skills necessary to create them. This book provides a simultaneous design blueprint, user guide, and research agenda for current and future developments and will appeal to a broad audience; from developers and users of data mining and grid technology, to advanced undergraduate and postgraduate students interested in this field.

Rahul Ramachandran, Sara Graves, John Rushing, Ken Keizer, Manil Maskey,
Hong Lin and Helen Conover ABSTRACT The Algorithm Development and
Mining System (ADaM) was originally developed in the early 1990s with the goal
of ...

Data Mining and Statistics for Decision Making

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.

This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, ...

Data Mining the Web

Uncovering Patterns in Web Content, Structure, and Usage

This book introduces the reader to methods of data mining on the web, including uncovering patterns in web content (classification, clustering, language processing), structure (graphs, hubs, metrics), and usage (modeling, sequence analysis, performance).

This book introduces the reader to methods of data mining on the web, including uncovering patterns in web content (classification, clustering, language processing), structure (graphs, hubs, metrics), and usage (modeling, sequence analysis, ...