Discovering Knowledge in Data

An Introduction to Data Mining
Author: Daniel T. Larose,Chantal D. Larose
Publisher: John Wiley & Sons
ISBN: 1118873572
Category: Computers
Page: 336
View: 2293

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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

Data mining

praktische Werkzeuge und Techniken für das maschinelle Lernen
Author: Ian H. Witten,Eibe Frank
Publisher: N.A
ISBN: 9783446215337
Category:
Page: 386
View: 2889

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Data Mining Methods and Models


Author: Daniel T. Larose
Publisher: John Wiley & Sons
ISBN: 0471756474
Category: Computers
Page: 385
View: 677

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Apply 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.

Data Mining and Predictive Analytics


Author: Daniel T. Larose
Publisher: John Wiley & Sons
ISBN: 1118868706
Category: Computers
Page: 824
View: 4694

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Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Advances in Knowledge Discovery and Data Mining


Author: Usama M. Fayyad
Publisher: Mit Press
ISBN: N.A
Category: Computers
Page: 611
View: 6681

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Eight sections of this book span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augumented database systems and application case studies. The appendices provide a list of terms used in the literature of the field of data mining and knowledge discovery in databases, and a list of online resources for the KDD researcher.

Biomedical Informatics

Discovering Knowledge in Big Data
Author: Andreas Holzinger
Publisher: Springer
ISBN: 3319045288
Category: Computers
Page: 551
View: 6615

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This book provides a broad overview of the topic Bioinformatics with focus on data, information and knowledge. From data acquisition and storage to visualization, ranging through privacy, regulatory and other practical and theoretical topics, the author touches several fundamental aspects of the innovative interface between Medical and Technology domains that is Biomedical Informatics. Each chapter starts by providing a useful inventory of definitions and commonly used acronyms for each topic and throughout the text, the reader finds several real-world examples, methodologies and ideas that complement the technical and theoretical background. This new edition includes new sections at the end of each chapter, called "future outlook and research avenues," providing pointers to future challenges. At the beginning of each chapter a new section called "key problems", has been added, where the author discusses possible traps and unsolvable or major problems.

Mining the Web

Discovering Knowledge from Hypertext Data
Author: Soumen Chakrabarti
Publisher: Morgan Kaufmann
ISBN: 9781558607545
Category: Computers
Page: 345
View: 8842

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The definitive book on mining the Web from the preeminent authority.

Knowledge Discovery in Spatial Data


Author: Yee Leung
Publisher: Springer Science & Business Media
ISBN: 9783642026645
Category: Social Science
Page: 360
View: 5438

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When I ?rst came across the term data mining and knowledge discovery in databases, I was excited and curious to ?nd out what it was all about. I was excited because the term tends to convey a new ?eld that is in the making. I was curious because I wondered what it was doing that the other ?elds of research, such as statistics and the broad ?eld of arti?cial intelligence, were not doing. After reading up on the literature, I have come to realize that it is not much different from conventional data analysis. The commonly used de?nition of knowledge discovery in databases: “the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” is actually in line with the core mission of conventional data analysis. The process employed by conventional data analysis is by no means trivial, and the patterns in data to be unraveled have, of course, to be valid, novel, useful and understandable. Therefore, what is the commotion all about? Careful scrutiny of the main lines of research in data mining and knowledge discovery again told me that they are not much different from that of conventional data analysis. Putting aside data warehousing and database m- agement aspects, again a main area of research in conventional database research, the rest of the tasks in data mining are largely the main concerns of conventional data analysis.

Database Support for Data Mining Applications

Discovering Knowledge with Inductive Queries
Author: Rosa Meo,Pier L. Lanzi,Mika Klemettinen
Publisher: Springer
ISBN: 3540444971
Category: Computers
Page: 332
View: 8892

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Data mining from traditional relational databases as well as from non-traditional ones such as semi-structured data, Web data, and scientific databases housing biological, linguistic, and sensor data has recently become a popular way of discovering hidden knowledge. This book on database support for data mining is developed to approaches exploiting the available database technology, declarative data mining, intelligent querying, and associated issues, such as optimization, indexing, query processing, languages, and constraints. Attention is also paid to the solution of data preprocessing problems, such as data cleaning, discretization, and sampling. The 16 reviewed full papers presented were carefully selected from various workshops and conferences to provide complete and competent coverage of the core issues. Some papers were developed within an EC funded project on discovering knowledge with inductive queries.

Statistik-Workshop für Programmierer


Author: Allen B. Downey
Publisher: O'Reilly Germany
ISBN: 3868993436
Category: Computers
Page: 160
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Wenn 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.

Knowledge Discovery, Knowledge Engineering and Knowledge Management

Third International Joint Conference, IC3K 2011, Paris, France, October 26-29, 2011. Revised Selected Papers
Author: Ana Fred,Jan Dietz,Kecheng Liu,Joaquim Filipe
Publisher: Springer
ISBN: 3642371868
Category: Computers
Page: 472
View: 5913

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This book constitutes the thoroughly refereed post-conference proceedings of the Third International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management, IC3K 2011, held in Paris, France, in October 2011. This book includes revised and extended versions of a strict selection of the best papers presented at the conference; 39 revised full papers together with one invited lecture were carefully reviewed and selected from 429 submissions. According to the three covered conferences KDIR 2011, KEOD 2011, and KMIS 2011, the papers are organized in topical sections on knowledge discovery and information retrieval, knowledge engineering and ontology development, and on knowledge management and information sharing.

Advances in Knowledge Discovery and Data Mining

12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 Proceedings
Author: Takashi Washio,Einoshin Suzuki,Kai Ming Ting,Akihiro Inokuchi
Publisher: Springer Science & Business Media
ISBN: 3540681248
Category: Computers
Page: 1102
View: 1704

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This book constitutes the refereed proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008, held in Osaka, Japan, in May 2008. The 37 revised long papers, 40 revised full papers, and 36 revised short papers presented together with 1 keynote talk and 4 invited lectures were carefully reviewed and selected from 312 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scientific discovery, data visualization, causal induction, and knowledge-based systems.

Methodologies for Knowledge Discovery and Data Mining

Third Pacific-Asia Conference, PAKDD'99, Beijing, China, April 26-28, 1999, Proceedings
Author: Ning Zhong,Lizhu Zhou
Publisher: Springer Science & Business Media
ISBN: 3540658661
Category: Computers
Page: 540
View: 8165

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This book constitutes the refereed proceedings of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD '99, held in Beijing, China, in April 1999. The 29 revised full papers presented together with 37 short papers were carefully selected from a total of 158 submissions. The book is divided into sections on emerging KDD technology; association rules; feature selection and generation; mining in semi-unstructured data; interestingness, surprisingness, and exceptions; rough sets, fuzzy logic, and neural networks; induction, classification, and clustering; visualization; causal models and graph-based methods; agent-based and distributed data mining; and advanced topics and new methodologies.

Data Mining and Medical Knowledge Management: Cases and Applications

Cases and Applications
Author: Berka, Petr
Publisher: IGI Global
ISBN: 1605662194
Category: Computers
Page: 464
View: 4455

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The healthcare industry produces a constant flow of data, creating a need for deep analysis of databases through data mining tools and techniques resulting in expanded medical research, diagnosis, and treatment. Data Mining and Medical Knowledge Management: Cases and Applications presents case studies on applications of various modern data mining methods in several important areas of medicine, covering classical data mining methods, elaborated approaches related to mining in electroencephalogram and electrocardiogram data, and methods related to mining in genetic data. A premier resource for those involved in data mining and medical knowledge management, this book tackles ethical issues related to cost-sensitive learning in medicine and produces theoretical contributions concerning general problems of data, information, knowledge, and ontologies.

Context Mediation Among Knowledge Discovery Components


Author: Alex Büchner
Publisher: Universal-Publishers
ISBN: 9781581122282
Category: Language Arts & Disciplines
Page: 216
View: 6629

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Context Mediation is a field of research that is concerned with the interchange of information across different environments, which provides a vehicle to bridge semantic gaps among disparate entities. Knowledge Discovery is concerned with the extraction of actionable information from large databases. A challenge that has received relatively little attention is knowledge discovery in a highly disparate environment, that is multiple heterogeneous data sources, multiple domain knowledge sources and multiple knowledge patterns. This thesis tackles the problem of semantic interoperability among data, domain knowledge and knowledge patterns in a knowledge discovery process using context mediation. All presented techniques, methods and models are applied in real-world scenarios, covering disciplines from a wide range of industry, namely web mining and marketing, manufacturing, meteorology and internationalisation. When feasible, industry standards were utilised, for instance ODMG, PMML and KQML. The carried out research has resulted in almost fifty international publications, including the co-authorship of a book, a journal editorship and one conference best paper award.

Wie kleine Kinder schlau werden

selbständiges Lernen im Alltag
Author: John Caldwell Holt
Publisher: Beltz
ISBN: 9783407228550
Category:
Page: 232
View: 1924

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Ausgehend von der Beobachtung des kindlichen Spielens erläutert der Autor, wie Kinder denken und lernen.