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Zdravko Markov Data Mining the Web


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).

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Giudici Paolo Applied Data Mining for Business and Industry


The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods. Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining. Features detailed case studies based on applied projects within industry. Incorporates discussion of data mining software, with case studies analysed using R. Is accessible to anyone with a basic knowledge of statistics or data analysis. Includes an extensive bibliography and pointers to further reading within the text. Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance.

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Jamie MacLennan Data Mining with Microsoft SQL Server 2008


Understand how to use the new features of Microsoft SQL Server 2008 for data mining by using the tools in Data Mining with Microsoft SQL Server 2008, which will show you how to use the SQL Server Data Mining Toolset with Office 2007 to mine and analyze data. Explore each of the major data mining algorithms, including naive bayes, decision trees, time series, clustering, association rules, and neural networks. Learn more about topics like mining OLAP databases, data mining with SQL Server Integration Services 2008, and using Microsoft data mining to solve business analysis problems.

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Sushmita Mitra Data Mining


First title to ever present soft computing approaches and their application in data mining, along with the traditional hard-computing approaches Addresses the principles of multimedia data compression techniques (for image, video, text) and their role in data mining Discusses principles and classical algorithms on string matching and their role in data mining

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Antonios Chorianopoulos Effective CRM using Predictive Analytics


A step-by-step guide to data mining applications in CRM. Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes. In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications. Key Features: Includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues. Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Accompanied by a website featuring material from each case study, including datasets and relevant code. Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM. Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers. The book will also be useful to academics and students interested in applied data mining.

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Pawel Cichosz Data Mining Algorithms


Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.

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Gordon Linoff S. Data Mining Techniques. For Marketing, Sales, and Customer Relationship Management


Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis

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Diane Cook J. Mining Graph Data


This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book you’ll be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets. There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be http://www.eecs.wsu.edu/MGD.

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Daniel Larose T. Discovering Knowledge in Data. An Introduction to Data Mining


The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book

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Mourad Elloumi Biological Knowledge Discovery Handbook


The first comprehensive overview of preprocessing, mining, and postprocessing of biological data Molecular biology is undergoing exponential growth in both the volume and complexity of biological data—and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD)—providing in-depth fundamental and technical field information on the most important topics encountered. Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing—also known as data mining—and data postprocessing) and analyzes both verification systems and discovery systems. BIOLOGICAL DATA PREPROCESSING Part A: Biological Data Management Part B: Biological Data Modeling Part C: Biological Feature Extraction Part D Biological Feature Selection BIOLOGICAL DATA MINING Part E: Regression Analysis of Biological Data Part F Biological Data Clustering Part G: Biological Data Classification Part H: Association Rules Learning from Biological Data Part I: Text Mining and Application to Biological Data Part J: High-Performance Computing for Biological Data Mining Combining sound theory with practical applications in molecular biology, Biological Knowledge Discovery Handbook is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.

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Zdravko Markov - Computer Science at CCSU

Talks, tutorials. Ingrid Russell, Zdravko Markov. An Introduction to the Weka Data Mining System. Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE 2017), Seattle, WA, USA, March 8-11, 2017.

Data Mining the Web (eBook, PDF) von Zdravko Markov ...

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).

Data Mining the Web: Uncovering Patterns in Web Content ...

Zdravko Markov, PhD, is Associate Professor of Computer Science at Central Connecticut State University. The author of three textbooks, Dr. Markov teaches undergraduate and graduate courses in computer science and artificial intelligence. He is currently a Principal Investigator (PI) in a National Science Foundation–funded project designed to introduce machine learning to undergraduates.

(PDF) Zdravko Markov and Daniel T. Larose, Data Mining the ...

Zdravko Markov and Daniel T. Larose, Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage

ZDRAVKO MARKOV AND DANIEL T. LAROSE - Lagout

Markov, Zdravko, 1956– Data-mining the Web : uncovering patterns in Web content, structure, and usage / by Zdravko, Markov & Daniel T. Larose. p. cm. Includes index. 978-0-471-66655-4 (cloth) 1. Data mining. 2. Web databases. I. Larose, Daniel T. II. Title. QA76.9.D343M38 2007 005.74 – dc22 2006025099 Printed in the United States of America 10987654321 iv. SPH SPH JWDD053-FM JWDD053-Markov ...

Zdravko MARKOV | Central Connecticut State University ...

Zdravko MARKOV of Central Connecticut State University, Connecticut (CCSU) | Read 61 publications | Contact Zdravko MARKOV

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The authors introduce 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).

Data Mining the Web : Zdravko Markov, : 9780470653470 ...

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).

Zdravko Markov and Daniel T. Larose, Data Mining the Web ...

Zdravko Markov and Daniel T. Larose, Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage. Wiley, New Britain, CT, 2007, 218 pp, $69.95. ISBN: 978-0-471-66655-4 . Max Chevalier 1 Information Retrieval volume 11, pages 169 – 174 (2008)Cite this article. 218 Accesses. 1 Citations. This is a preview of subscription content, log in to check access. Access options Buy ...

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‪Zdravko Markov‬ - ‪Google Scholar‬

‪Professor of Computer Science, Central Connecticut State University‬ - ‪Cited by 806‬ - ‪Machine Learning‬ - ‪Data Mining‬ - ‪Web Mining‬ - ‪Artificial Intelligence‬

CS 580 - Data Mining - Computer Science at CCSU

Assignments, projects and grading: There will be 8 projects requiring independent study and practical work with a data mining system for solving data mining tasks and 2 quizzes. The final grade will be based 80% on projects and 20% on quizzes. The letter grades will be calculated according to the following table:

Data Mining the Web : Uncovering Patterns in Web Content ...

[(Data Mining the Web : Uncovering Patterns in Web Content, Structure, and Usage)] [By (author) Zdravko Markov ] published on (May, 2007) | Zdravko Markov | ISBN: | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

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Data-Mining the Web book. Read reviews from world’s largest community for readers. This book introduces the reader to methods of data mining on the web, ...

Zdravko Markov (Author of Data-Mining the Web)

Zdravko Markov is the author of Data-Mining the Web (3.00 avg rating, 5 ratings, 0 reviews, published 2007), Proceedings of the Twenty-Ninth Internationa...

Data Mining the Web: Uncovering Patterns in Web Content ...

Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage by Zdravko Markov (2007-04-25) | Zdravko Markov;Daniel T. Larose | ISBN: | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

Data Mining the Web: Uncovering Patterns in Web Content ...

Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage - Ebook written by Zdravko Markov, Daniel T. Larose. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage.

Zdravko Markov at Central Connecticut State University ...

Zdravko Markov. Professor in the Computer Science department at Central Connecticut State University. 100%. Would take again . 2.4. Level of Difficulty. Rate Professor Markov. I'm Professor MarkovSubmit a Correction. Professor Markov's Top Tags. EXTRA CREDIT Gives good feedback Clear grading criteria Accessible outside class Would take again 😎 Most helpful rating: CS151. Feb 14th, 2018 ...

Data Mining the Web: Uncovering Patterns in Web Content ...

Zdravko Markov, Daniel T. Larose Limited preview - 2007. Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage Zdravko Markov, Daniel T. Larose No preview available - 2007. Common terms and phrases. approach association rules average Boolean browser C4.5 algorithm CCSU web log centroid Chapter class labels classification cluster centroids coefficient common log format ...

Zdravko Markov - DBLP

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Data Mining the Web : Zdravko Markov : 9780471666554

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Data Mining the Web: Uncovering Patterns in Web Content ...

Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage Zdravko Markov , Daniel T. Larose Wiley , 2007 , xvi + 218 pages, £ 38.95 /€ 56.00, hardcover ISBN: 978‐0‐471‐66655‐4 Table of contents Part I: Web structure mining Part III: Web usage mining 1. Information retrieval and web search 6. Introduction to web usage mining 2. Hyperlink‐based ranking 7 ...

Data Mining the Web : Zdravko Markov : 9780470108093

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Zdravko Markov: free download. Ebooks library. On-line ...

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Data Mining the Web - ISBN: 9780470108086 - (ebook) - von Zdravko Markov, Daniel T. Larose, Verlag: Wiley

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Home Pages - uni-sofia.bg

Zdravko Markov. Faculty of Mathematics and Informatics - Department of Information Technology University of Sofia 5 James Bouchier Str., 1164 Sofia, Bulgaria Phone: (+ 359 2) 6256 861, Fax: (+ 359 2) 656 157

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(PDF) An introduction to the WEKA data mining system ...

An introduction to the WEKA data mining system

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Albalate Amparo Semi-Supervised and Unsupervised Machine Learning. Novel Strategies


This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described.

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Gordon Linoff S. Data Mining Techniques. For Marketing, Sales, and Customer Relationship Management


The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition—more than 50% new and revised— is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more Provides best practices for performing data mining using simple tools such as Excel Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.

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Paul Attewell Data Mining for the Social Sciences


We live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining methodologies in their analytical toolkits. Data Mining for the Social Sciences demystifies the process by describing the diverse set of techniques available, discussing the strengths and weaknesses of various approaches, and giving practical demonstrations of how to carry out analyses using tools in various statistical software packages.

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Russell Anderson K. Visual Data Mining. The VisMiner Approach


A visual approach to data mining. Data mining has been defined as the search for useful and previously unknown patterns in large datasets, yet when faced with the task of mining a large dataset, it is not always obvious where to start and how to proceed. This book introduces a visual methodology for data mining demonstrating the application of methodology along with a sequence of exercises using VisMiner. VisMiner has been developed by the author and provides a powerful visual data mining tool enabling the reader to see the data that they are working on and to visually evaluate the models created from the data. Key features: Presents visual support for all phases of data mining including dataset preparation. Provides a comprehensive set of non-trivial datasets and problems with accompanying software. Features 3-D visualizations of multi-dimensional datasets. Gives support for spatial data analysis with GIS like features. Describes data mining algorithms with guidance on when and how to use. Accompanied by VisMiner, a visual software tool for data mining, developed specifically to bridge the gap between theory and practice. Visual Data Mining: The VisMiner Approach is designed as a hands-on work book to introduce the methodologies to students in data mining, advanced statistics, and business intelligence courses. This book provides a set of tutorials, exercises, and case studies that support students in learning data mining processes. In praise of the VisMiner approach: «What we discovered among students was that the visualization concepts and tools brought the analysis alive in a way that was broadly understood and could be used to make sound decisions with greater certainty about the outcomes» —Dr. James V. Hansen, J. Owen Cherrington Professor, Marriott School, Brigham Young University, USA «Students learn best when they are able to visualize relationships between data and results during the data mining process. VisMiner is easy to learn and yet offers great visualization capabilities throughout the data mining process. My students liked it very much and so did I.» —Dr. Douglas Dean, Assoc. Professor of Information Systems, Marriott School, Brigham Young University, USA

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Wayne Johnson P. Making Sense of Data II


A hands-on guide to making valuable decisions from data using advanced data mining methods and techniques This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more technical topics, this book equips readers with advanced data mining methods that are needed to successfully translate raw data into smart decisions across various fields of research including business, engineering, finance, and the social sciences. Following a comprehensive introduction that details how to define a problem, perform an analysis, and deploy the results, Making Sense of Data II addresses the following key techniques for advanced data analysis: Data Visualization reviews principles and methods for understanding and communicating data through the use of visualization including single variables, the relationship between two or more variables, groupings in data, and dynamic approaches to interacting with data through graphical user interfaces. Clustering outlines common approaches to clustering data sets and provides detailed explanations of methods for determining the distance between observations and procedures for clustering observations. Agglomerative hierarchical clustering, partitioned-based clustering, and fuzzy clustering are also discussed. Predictive Analytics presents a discussion on how to build and assess models, along with a series of predictive analytics that can be used in a variety of situations including principal component analysis, multiple linear regression, discriminate analysis, logistic regression, and Naïve Bayes. Applications demonstrates the current uses of data mining across a wide range of industries and features case studies that illustrate the related applications in real-world scenarios. Each method is discussed within the context of a data mining process including defining the problem and deploying the results, and readers are provided with guidance on when and how each method should be used. The related Web site for the series (www.makingsenseofdata.com) provides a hands-on data analysis and data mining experience. Readers wishing to gain more practical experience will benefit from the tutorial section of the book in conjunction with the TraceisTM software, which is freely available online. With its comprehensive collection of advanced data mining methods coupled with tutorials for applications in a range of fields, Making Sense of Data II is an indispensable book for courses on data analysis and data mining at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who are interested in learning how to accomplish effective decision making from data and understanding if data analysis and data mining methods could help their organization.

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И. И. Холод Анализ данных и процессов


Излагаются основные направления в области разработки корпоративных систем: организация хранилищ данных, оперативный (OLAP) и интеллектуальный (Data Mining) анализ данных. В третьем издании по сравнению со вторым, выходившем под названием «Технологии анализа данных: Data Mining, Text Mining, Visual Mining, OLAP», добавлены визуальный (Visual Mining) и текстовый (Text Mining) анализ данных, анализ процессов (Process Mining), анализ Web-ресурсов (Web mining) и анализ в режиме реального времени (Real-Time Data Mining). Приведено описание методов и алгоритмов решения основных задач анализа: классификации, кластеризации и др. Описание идеи каждого метода дополняется конкретным примером его использования. Для студентов, инженеров и специалистов в области анализа данных и процессов. (Компакт-диск прилагается только к печатному изданию.)

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Tsiptsis Konstantinos K. Data Mining Techniques in CRM. Inside Customer Segmentation


This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.

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Meta Brown S. Data Mining For Dummies


Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business's entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn't take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business's needs. In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including: Model creation, validity testing, and interpretation Effective communication of findings Available tools, both paid and open-source Data selection, transformation, and evaluation Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. If you're serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining.

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Leading experts illustrate how sophisticated computational data mining techniques can impact contemporary drug discovery and development In the era of post-genomic drug development, extracting and applying knowledge from chemical, biological, and clinical data is one of the greatest challenges facing the pharmaceutical industry. Pharmaceutical Data Mining brings together contributions from leading academic and industrial scientists, who address both the implementation of new data mining technologies and application issues in the industry. This accessible, comprehensive collection discusses important theoretical and practical aspects of pharmaceutical data mining, focusing on diverse approaches for drug discovery—including chemogenomics, toxicogenomics, and individual drug response prediction. The five main sections of this volume cover: A general overview of the discipline, from its foundations to contemporary industrial applications Chemoinformatics-based applications Bioinformatics-based applications Data mining methods in clinical development Data mining algorithms, technologies, and software tools, with emphasis on advanced algorithms and software that are currently used in the industry or represent promising approaches In one concentrated reference, Pharmaceutical Data Mining reveals the role and possibilities of these sophisticated techniques in contemporary drug discovery and development. It is ideal for graduate-level courses covering pharmaceutical science, computational chemistry, and bioinformatics. In addition, it provides insight to pharmaceutical scientists, principal investigators, principal scientists, research directors, and all scientists working in the field of drug discovery and development and associated industries.

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