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Hugo Kubinyi Data Mining in Drug Discovery

Written for drug developers rather than computer scientists, this monograph adopts a systematic approach to mining scientifi c data sources, covering all key steps in rational drug discovery, from compound screening to lead compound selection and personalized medicine. Clearly divided into four sections, the first part discusses the different data sources available, both commercial and non-commercial, while the next section looks at the role and value of data mining in drug discovery. The third part compares the most common applications and strategies for polypharmacology, where data mining can substantially enhance the research effort. The final section of the book is devoted to systems biology approaches for compound testing. Throughout the book, industrial and academic drug discovery strategies are addressed, with contributors coming from both areas, enabling an informed decision on when and which data mining tools to use for one's own drug discovery project.

14601.18 RUR



Ekins Sean Pharmaceutical Data Mining. Approaches and Applications for Drug Discovery

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.

12062.98 RUR



Hugo Kubinyi Drug Metabolism Prediction

The first professional reference on this highly relevant topic, for drug developers, pharmacologists and toxicologists. The authors provide more than a systematic overview of computational tools and knowledge bases for drug metabolism research and their underlying principles. They aim to convey their expert knowledge distilled from many years of experience in the field. In addition to the fundamentals, computational approaches and their applications, this volume provides expert accounts of the latest experimental methods for investigating drug metabolism in four dedicated chapters. The authors discuss the most important caveats and common errors to consider when working with experimental data. Collating the knowledge gained over the past decade, this practice-oriented guide presents methods not only used in drug development, but also in the development and toxicological assessment of cosmetics, functional foods, agrochemicals, and additives for consumer goods, making it an invaluable reference in a variety of disciplines.

15271.2 RUR



Hugo Kubinyi Drug-Membrane Interactions. Analysis, Drug Distribution, Modeling

Barrier, reservoir, target site – those are but some of the possible functions of biological lipid membranes in the complex interplay of drugs with the organism. A detailed knowledge of lipid membranes and of the various modes of drug-membrane interaction is therefore the prerequisite for a better understanding of drug action. Many of today's pharmaceuticals are amphiphilic or catamphiphilic, enabling them to interact with biological membranes. Crucial membrane properties are surveyed and techniques to elucidate drug-membrane interactions presented, including computer-aided predictions. Effects of membrane interaction on drug action and drug distribution are discussed, and numerous examples are given. This unique reference volume builds on the authors' long experience in the study of drug-membrane interaction. Recommended reading for everyone involved in pharmaceutical research.

11174.05 RUR



Enrico Seib Data Mining - Methoden in der Simulation

Bachelorarbeit aus dem Jahr 2008 im Fachbereich Informatik - Wirtschaftsinformatik, Note: 1,0, Universität Rostock (Institut für Informatik, Lehrstuhl für Modellierung und Simulation), 100 Quellen im Literaturverzeichnis, Sprache: Deutsch, Abstract: Principles and methods of data mining are a widespread area, i.e. retail dealer use data mining tools to analyze the behavior of customers, computer hardware supplier use data mining to optimize their inventory. There are multiple possibilities of using data mining techniques, even in technical and scientific areas of applications. In regard of manyfold fields of application, there are no less than the number of techniques and methods for Data Mining in existence. Another field to apply Data Mining technique is the domain of simulation. Simulation is the computer-based approach of executing and experimenting of and with models. One aim of this thesis is to analyze data mining tools to see how capable they are solving data mining duties with respect to data calculated by simulation. Different data mining tools are analyzed, commercial tools like SPSS and SPSS Clementine as well as established and freely available tools like WEKA and the R-Project. These tools are analyzed in matters of their data mining functionalities, options to access different data sources, and their complexity of different data mining algorithms. Beyond the analysis of data mining tools with respect to functionality and simulation, envi-ronments for modeling a...

4752 RUR



Hugo Kubinyi Prodrugs and Targeted Delivery. Towards Better ADME Properties

This topical reference and handbook addresses the chemistry, pharmacology, toxicology and the patentability of prodrugs, perfectly mirroring the integrated approach prevalent in today's drug design. It summarizes current experiences and strategies for the rational design of prodrugs, beginning at the early stages of the development process, as well as discussing organ- and site-selective prodrugs. Every company employing medicinal chemists will be interested in this practice-oriented overview of a key strategy in modern drug discovery and development.

15643.67 RUR



Mourad Elloumi Biological Knowledge Discovery Handbook. Preprocessing, Mining and Postprocessing of Data

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.

13930.46 RUR



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.

4071 RUR



Hugo Kubinyi Drug Bioavailability. Estimation of Solubility, Permeability, Absorption and Bioavailability

The gold standard for industrial research now completely revised in line with current trends in the field, with all contributions extensively updated or rewritten. In 21 chapters readers can benefit from the key working knowledge of today's leading pharmaceutical companies, including Pfizer, AstraZeneca, and Roche. Drug developers from industry and academia present all the factors governing drug bioavailability, complete with practical examples and real-life data. Part I focuses on in vitro and in vivo measurements of physicochemical properties, such as membrane permeability and ionization. Part II discusses solubility and gastrointestinal absorption, while the third part is devoted to metabolism and excretory mechanisms. The much revised and expanded part IV surveys current in silico approaches to predict drug properties needed to estimate the bioavailability of any new drug candidate. The final part shows how poor bioavailability may be improved by various approaches during the development process. No other publication offers the same level of treatment on this crucial topic in modern drug development.

21156.48 RUR



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.

13944.46 RUR



Pawel Cichosz Data Mining Algorithms. Explained Using R

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.

5959.49 RUR



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

7007.84 RUR



El Amir Eman Web Services Approach for GeoSpatial Data Mining

Geospatial Data Mining describes the combination of two key market intelligence software tools: Geographical Information Systems and Data Mining Systems. GIS and Data Mining are naturally synergistic technologies that can be synthesized to produce powerful market insight from a sea of disparate data. This book describes a research that developed a Spatial Data Mining Web Service. It integrates state of the art Geographic Information Systems and Data Mining Systems functionality in an open, highly extensible, internet-enabled plug-in architecture. within the book you will learn that one analysis can often lead into others, parameters for tools may change, criteria for analyses can evolve, or you may want to perform additional visual analysis on the results to make them more meaningful or easier to interpret.

7552 RUR



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Hengqing Tong Developing Econometrics

Statistical Theories and Methods with Applications to Economics and Business highlights recent advances in statistical theory and methods that benefit econometric practice. It deals with exploratory data analysis, a prerequisite to statistical modelling and part of data mining. It provides recently developed computational tools useful for data mining, analysing the reasons to do data mining and the best techniques to use in a given situation. Provides a detailed description of computer algorithms. Provides recently developed computational tools useful for data mining Highlights recent advances in statistical theory and methods that benefit econometric practice. Features examples with real life data. Accompanying software featuring DASC (Data Analysis and Statistical Computing). Essential reading for practitioners in any area of econometrics; business analysts involved in economics and management; and Graduate students and researchers in economics and statistics.

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Samira ElAtia Data Mining and Learning Analytics. Applications in Educational Research

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

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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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Hendrik Eisenberg Statistische Methoden des Data Mining und deren Anwendung

Inhaltsangabe:Zusammenfassung: In dieser Arbeit stehen neben dem Begriff des „Data Mining“ besonders die statistischen Methoden im Mittelpunkt. Interessenten sollen den kreativen Prozess des Data Mining näher kennen lernen und erfahren, welche Rolle dabei der Statistik zukommt. Das Ziel der Arbeit ist, eine weiterreichende Darstellung des Prozesses des Data Mining mit statistischen Methoden zu erstellen, angefangen bei der Zielfindung, über die Modellbildung, bis hin zur Bewertung der Ergebnisse. Dabei orientiert sich die Vorgehensweise der systematischen Auswertung an der Methode des CRoss Industry Standard Process for Data Mining, mit der sich Data Mining Prozesse beschreiben lassen. Zum besseren Verständnis werden grundlegende Begriffe zum Data Mining sowie die bedeutsamsten Methoden und Verfahren zur statistischen Datenanalyse erläutert, welche bei den im Anschluss aufgezeigten Data Mining Problemen zur Anwendung kommen. Die veranschaulichten Analyseprobleme entsprechen den Aufgaben der Data Mining Cups der Jahre 2001 und 2002. Dabei werden die zur Lösung angewendeten statistischen Methoden nachvollziehbar wiedergegeben und es wird auf die kritischen Erfolgsfaktoren eingegangen. Oftmals wirken sich schon einzelne Teilentscheidungen bei der Datenaufbereitung und bei den eingesetzten Klassifizierungsmethoden auf die Lösung der Data Mining Aufgabe aus. Daher stellte sich die Frage, wie solche Abweichungen von den aufgezeigten Methoden aussehen könnten. In dieser Arbeit werde...

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Hina Kanth, Aiman Mushtaq, Rafi Ahmad Khan Data Mining for Marketing

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Hugo Kubinyi Phosphodiesterases and Their Inhibitors

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Hugo Kubinyi Thermodynamics and Kinetics of Drug Binding

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Andre Hiller Einfuhrung in den Einsatz von Data Mining

Diplomarbeit aus dem Jahr 2003 im Fachbereich Statistik, Note: 1,3, Hochschule Anhalt - Standort Bernburg, 16 Quellen im Literaturverzeichnis, Sprache: Deutsch, Abstract: Diese Arbeit soll eine Einführung in den Einsatz von Data Mining bei der Störungsbeseitigung geben. Als Testobjekt wurde die Störungsdatenbank der SOLVAY Deutschland GmbH in Bernburg gewählt. Es soll geprüft werden, ob diese die Voraussetzungen für Data Mining - Analysen erfüllt und welche Ergebnisse erzielt werden können. Des Weiteren soll ein Ausblick auf weitere Möglichkeiten des Data Mining - Einsatzes gegeben werden. Das Interesse an Data Mining wurde durch ständig auftauchende Berichte, abgehaltene Konferenzen und Wettbewerbe geweckt. Wie z.B. durch den Artikel von Michael Gonzales, dem Geschäftsführer der „Focus Group, Ltd „ einer Unternehmensberatung, die sich auf Data Mining spezialisiert hat. In seinem Artikel beschreibt er die Notwendigkeit der Nutzung von Data Mining für die Bewältigung der Informationsflut und den daraus entstehenden Informationsvorsprung gegenüber anderen Unternehmen. Seiner Meinung ist die Bereitstellung der Daten durch ein Data Warehouse die wichtigste Voraussetzung für die Durchführung von Data Mining - Projekten, da 80% der benötigten Zeit für die Datenaufbereitung benötigt werden. Durch die richtige Vorbereitung der Daten können schneller Ergebnisse erzielt werden und diese für die Entscheidungsfindung genutzt werden.

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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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Contemporary Perspectives in Data Mining

The series, Contemporary Perspectives on Data Mining, is composed of blindrefereed scholarly research methods and applications of data mining. This serieswill be targeted both at the academic community, as well as the businesspractitioner.Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematicaltechniques. The knowledge is extracted form this data by examining the patterns of the data, whether they beassociations of groups or things, predictions, sequential relationships between time order events or naturalgroups.Data mining applications are seen in finance (banking, brokerage, insurance), marketing (customer relationships,retailing, logistics, travel), as well as in manufacturing, health care, fraud detection, home-land security, and lawenforcement.

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Yasin Yakut Erzielen von Wettbewerbsvorteilen durch Data Mining in Produktion und Logistik

Diese Studie widmet sich dem Thema „Anwendungsfelder für Data Mining in Produktion und Logistik". Data Mining Verfahren sind in der Praxis weit verbreitet und unterstützen mit wertvollem Wissen Geschäftsführungen bei Ihren Entscheidungen. Vor allem aus den Bereichen Vertrieb und Marketing ist Data Mining nicht mehr wegzudenken. Anhand von vorhandenen Daten können z.B. Informationen generiert werden, um verfeinerte Werbestrategien zu bilden, die umsatzfördernd sind. Da aber auch in der Produktion und Logistik durch die zunehmende Automatisierung Unmengen an Daten anfallen, ergibt sich hier ebenfalls die Möglichkeit diese nach Optimierungspotential zu untersuchen. In diesem Zusammenhang soll diese Studie bei der Aufdeckung neuer Einsatzmöglichkeiten mitwirken.Einige Unternehmen setzen mittlerweile Data Mining Methoden effizient in der Produktions- bzw. Logistikkette ein und profitieren von den Vorteilen: Kostspielige Reparaturen werden durch präventive Wartungen vermieden;Fehler werden schneller diagnostiziert und gezielt beseitigt; Lagerkosten können mit den geeigneten Erkenntnissen gesenkt werden.All das verborgene Wissen in den Datenbergen könnte Unternehmen durch den Einsatz von Data Mining Methoden viel Geld, Zeit und vor allem Nerven ersparen. In dieser Untersuchung wird zudem aufbauend auf den Erkenntnissen einer Online-Befragung, der Einsatz von Data Mining in einem der potentiellen Anwendungsbereiche beispielhaft umgesetzt und hinsichtlich der erzielbaren Potential...

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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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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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