Publisher: Pearson Education India
Publisher: Pearson Education India
Author: Pang-Ning Tan,Michael Steinbach,Vipin Kumar
Publisher: Pearson Higher Ed
View: 1168Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. Quotes This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts. -Sanjay Ranka, University of Florida In my opinion this is currently the best data mining text book on the market. I like the comprehensive coverage which spans all major data mining techniques including classification, clustering, and pattern mining (association rules). -Mohammed Zaki, Rensselaer Polytechnic Institute
Author: G. K. GUPTA
Publisher: PHI Learning Pvt. Ltd.
View: 724The field of data mining provides techniques for automated discovery of valuable information from the accumulated data of computerized operations of enterprises. This book offers a clear and comprehensive introduction to both data mining theory and practice. It is written primarily as a textbook for the students of computer science, management, computer applications, and information technology. The book ensures that the students learn the major data mining techniques even if they do not have a strong mathematical background. The techniques include data pre-processing, association rule mining, supervised classification, cluster analysis, web data mining, search engine query mining, data warehousing and OLAP. To enhance the understanding of the concepts introduced, and to show how the techniques described in the book are used in practice, each chapter is followed by one or two case studies that have been published in scholarly journals. Most case studies deal with real business problems (for example, marketing, e-commerce, CRM). Studying the case studies provides the reader with a greater insight into the data mining techniques. The book also provides many examples, review questions, multiple choice questions, chapter-end exercises and a good list of references and Web resources especially those which are easy to understand and useful for students. A number of class projects have also been included.
An Introduction to Data Mining
Author: Daniel T. Larose
Publisher: John Wiley & Sons
Author: Chaitanya P Agrawal, Meena Agrawal
Publisher: Educreation Publishing
View: 5892This book is a small endeavor to share the journey of getting introduced to a wonderful topic Data Mining. Personally we came across this during the process of evaluating new tools to be included in the post graduate study curricula of the University we are working in. Soon it became a friendly affair to see the power, potential and ease of empowering the databases with concepts of data mining. It has become powerful in rediscovering the hidden values in data base and soon in data warehouse, equally efficiently. The Data mining is a powerful new technology with great potential focusing on the most important information in their data warehouses. It involves extraction of hidden predictive information from large databases with ease and efficiency. It facilitates to make proactive, knowledge-driven decisions and predict future trends and behaviors. Data mining tools move beyond the analyses of past events provided by retrospective tools typical of decision support systems. The automated, prospective analyses offered by data mining tools can answer finding predictive information easily. This small book is an introduction to the basics of data mining. It also introduces the techniques and technologies behind data mining, the impact of artificial intelligence, artificial neural networks, and fuzzy logic et cetera as the basic building blocks for the same. It concludes with common practical applications, trends and its impact on social and computing environment.
Author: S. Sumathi,S.N. Sivanandam
Publisher: Springer Science & Business Media
View: 1666This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data. It is also written for technical managers and executives as well as for technologists interested in learning about data mining.
praktische Werkzeuge und Techniken für das maschinelle Lernen
Author: Ian H. Witten,Eibe Frank
Author: Rob Sullivan
Publisher: Springer Science & Business Media
View: 7349Data mining provides a set of new techniques to integrate, synthesize, and analyze tdata, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization.
Author: David Olson,Yong Shi
Publisher: Irwin Professional Publishing
View: 3429Introduction to Business Data Mining was developed to introduce students, as opposed to professional practitioners or engineering students, to the fundamental concepts of data mining. Most importantly, this text shows readers how to gather and analyze large sets of data to gain useful business understanding. A four part organization introduces the material (Part I), describes and demonstrated basic data mining algorithms (Part II), focuses on the business applications of data mining (Part III), and presents an overview of the developing areas in this field, including web mining, text mining, and the ethical aspects of data mining. (Part IV).The author team has had extensive experience with the quantitative analysis of business as well as with data mining analysis. They have both taught this material and used their own graduate students to prepare the textâ€™s data mining reports. Using real-world vignettes and their extensive knowledge of this new subject, David Olson and Yong Shi have created a text that demonstrates data mining processes and techniques needed for business applications.
A Systematic Introduction to Concepts and Theory
Author: Zhongfei Zhang,Ruofei Zhang
Publisher: CRC Press
View: 2611Collecting the latest developments in the field, Multimedia Data Mining: A Systematic Introduction to Concepts and Theory defines multimedia data mining, its theory, and its applications. Two of the most active researchers in multimedia data mining explore how this young area has rapidly developed in recent years. The book first discusses the theoretical foundations of multimedia data mining, presenting commonly used feature representation, knowledge representation, statistical learning, and soft computing techniques. It then provides application examples that showcase the great potential of multimedia data mining technologies. In this part, the authors show how to develop a semantic repository training method and a concept discovery method in an imagery database. They demonstrate how knowledge discovery helps achieve the goal of imagery annotation. The authors also describe an effective solution to large-scale video search, along with an application of audio data classification and categorization. This novel, self-contained book examines how the merging of multimedia and data mining research can promote the understanding and advance the development of knowledge discovery in multimedia data.
An Introduction to the Computational Analysis of Content
Author: Kalev Leetaru
Category: Language Arts & Disciplines
View: 8146With 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.
Author: Sang C. Suh
Publisher: Jones & Bartlett Publishers
View: 1539Various topics of data mining techniques are identified and described throughout, including clustering, association rules, rough set theory, probability theory, neural networks, classification, and fuzzy logic. Each of these techniques is explored with a theoretical introduction and its effectiveness is demonstrated with various chapter examples.
Author: Adelchi Azzalini,Bruno Scarpa
Publisher: OUP USA
Category: Business & Economics
View: 6143This book introduces the specific statistical methods and models involved in data mining, targeting students and professionals with a a basic knowledge of statistical reasoning.
Author: David J. Hand,Heikki Mannila,Professor in the Department of Statistics David J Hand,Padhraic Smyth
Publisher: MIT Press
View: 3818Measuremente and Data. Visualizing and Exploring Data. Data Analysis and Uncertainty. A Systematic Overview of Data Mining Algorithms. Models and Patterns. Score Functions for Data Mining Algorithms. Serach and Optimization Methods. Descriptive Modeling. Predictive Modeling for Classification. Predictive Modeling for Regression. Data Organization and Databases. Finding Patterns and Rules. Retrieval by Content.
Author: Paul Attewell,David Monaghan
Publisher: Univ of California Press
Category: Social Science
View: 1050We 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.
Author: Patricia B. Cerrito
Publisher: SAS Institute
View: 9532If you have an abundance of data, but no idea what to do with it, this book was written for you! Packed with examples from an array of industries, Introduction to Data Mining Using SAS Enterprise Miner provides you with excellent starting points and practical guidelines to begin data mining today. Author Patricia Cerrito encourages you to think of data mining as a process of exploration rather than as a collection of tools to investigate data. In that way, you choose the methods that will extract the most information from your data, and, while there are no right answers to investigating data sets, there are many questions that can be asked to produce meaningful results. Each answer then creates a path that helps you drill down to explore the data fully. It is up to you to determine what is of interest and what is important to analyze.
Author: Daniel T. Larose
Publisher: John Wiley & Sons
View: 8735Apply powerful Data Mining Methods and Models to Leverage your Data for Actionable Results Data Mining Methods and Models provides: * The latest techniques for uncovering hidden nuggets of information * The insight into how the data mining algorithms actually work * The hands-on experience of performing data mining on large data sets Data Mining Methods and Models: * Applies a "white box" methodology, emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets, including a detailed case study, "Modeling Response to Direct-Mail Marketing" * Tests the reader's level of understanding of the concepts and methodologies, with over 110 chapter exercises * Demonstrates the Clementine data mining software suite, WEKA open source data mining software, SPSS statistical software, and Minitab statistical software * Includes a companion Web site, www.dataminingconsultant.com, where the data sets used in the book may be downloaded, along with a comprehensive set of data mining resources. Faculty adopters of the book have access to an array of helpful resources, including solutions to all exercises, a PowerPoint(r) presentation of each chapter, sample data mining course projects and accompanying data sets, and multiple-choice chapter quizzes. With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field. An Instructor's Manual presenting detailed solutions to all the problems in the book is available onlne.
A Guide to Productivity
Author: Stephan Kudyba,Richard Hoptroff
Publisher: IGI Global
View: 2508Annotation Provides an overview of data mining technology and how it is applied in a business environment. Material is not written in a technical style, but rather addresses the applied methodology behind implementing data mining techniques in the corporate environment. Explains how the technology evolved, overviews the methodologies that comprise the data mining spectrum, and looks at everyday business applications for data mining, in areas such as marketing and advertising promotions and pricing policies using econometric-based modeling, and using the Internet to help improve an organization's performance. Kudyba is an economic consultant. Hoptroff is an independent consultant with experience in data mining software. Annotation c. Book News, Inc., Portland, OR (booknews.com).
Author: Allen B. Downey
Publisher: O'Reilly Germany
View: 4752Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.