...Data Mining Third Edition This page intentionally left blank Data Mining Practical Machine Learning Tools and Techniques Third Edition Ian H. Witten Eibe Frank Mark A. Hall AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Morgan Kaufmann Publishers is an imprint of Elsevier Morgan Kaufmann Publishers is an imprint of Elsevier 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA This book is printed on acid-free paper. Copyright © 2011 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must...
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...identify and evaluate data mining algorithms which are commonly implemented in modern Medical Decision Support Systems (MDSS). They are used in various healthcare units all over the world. These institutions store large amounts of medical data. This data may contain relevant medical information hidden in various patterns buried among the records. Within the research several popular MDSS’s are analysed in order to determine the most common data mining algorithms utilized by them. Three algorithms have been identified: Naïve Bayes, Multilayer Perceptron and C4.5. Prior to the very analyses the algorithms are calibrated. Several testing configurations are tested in order to determine the best setting for the algorithms. Afterwards, an ultimate comparison of the algorithms orders them with respect to their performance. The evaluation is based on a set of performance metrics. The analyses are conducted in WEKA on five UCI medical datasets: breast cancer, hepatitis, heart disease, dermatology disease, diabetes. The analyses have shown that it is very difficult to name a single data mining algorithm to be the most suitable for the medical data. The results gained for the algorithms were very similar. However, the final evaluation of the outcomes allowed singling out the Naïve Bayes to be the best classifier for the given domain. It was followed by the Multilayer Perceptron and the C4.5. Keywords: Naïve Bayes, Multilayer Perceptron, C4.5, medical data mining, medical decision...
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...The major emphasis of accreditation process is to measure the outcomes of the program that is being accredited. In line with this Faculty of Technology of University of Mumbai has taken a lead in incorporating philosophy of outcome based education in the process of curriculum development. Faculty of Technology, University of Mumbai, in one of its meeting unanimously resolved that, each Board of Studies shall prepare some Program Educational Objectives (PEO‟s) and give freedom to affiliated Institutes to add few (PEO‟s) and course objectives and course outcomes to be clearly defined for each course, so that all faculty members in affiliated institutes understand the depth and approach of course to be taught, which will enhance learner‟s learning process. It was also resolved that, maximum senior faculty from colleges and experts from industry to be involved while revising the curriculum. I am happy to state that, each Board of studies has adhered to the resolutions passed by Faculty of Technology, and developed curriculum accordingly. In addition to outcome based education, semester based credit and grading system is also introduced to ensure quality of engineering education. Semester based Credit and Grading system enables a much-required shift in focus from teacher-centric to learner-centric...
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...environmental monitoring system Ioannis N. Athanasiadis and Pericles A. Mitkas ABSTRACT Fairly rapid environmental changes call for continuous surveillance and on- line decision- making. Two areas where IT technologies can be valuable. In this paper we present a multi-agent system for monitoring and assessing air-quality attributes, which uses data coming from a meteorological station. A community of software agents is assigned to monitor and validate measurements coming from several sensors, to assess air-quality, and, finally, to fire alarms to appropriate recipients, when needed. Data mining techniques have been used for adding data-driven, customized intelligence into agents. The architecture of the developed system, its domain ontology, and typical agent interactions are presented. Finally, the deployment of a real-world test case is demonstrated. Keywords : Multi-Agent Systems, Intelligent Applications, Data Mining, Inductive Agents, Air-Quality Monitoring Introduction Environmental Information Systems (EIS) is a generic term that describes the class of systems that perform one or more of the following tasks: environmental monitoring, data storage and access, disaster description and response, environmental reporting, planning and simulation, modeling and decision- making. As the requirements for accurate and timely information in these systems are increasing, the need for incorporating advanced, intelligent features in EIS is revealed. In this context advances in Information...
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...TURBMW06_013234761X.QXD 3/7/07 8:07 PM ONLINE CHAPTER Page 1 Neural Networks 6 for Data Mining Learning Objectives ◆ Understand the concept and different types of artificial neural networks (ANN) ◆ Learn the advantages and limitations of ANN ◆ Understand how backpropagation neural networks learn ◆ Understand the complete process of using neural networks ◆ Appreciate the wide variety of applications of neural networks N eural networks have emerged as advanced data mining tools in cases where other techniques may not produce satisfactory predictive models. As the term implies, neural networks have a biologically inspired modeling capability, but are essentially statistical modeling tools. In this chapter, we study the basics of neural network modeling, some specific applications, and the process of implementing a neural network project. 6.1 Opening Vignette: Using Neural Networks to Predict Beer Flavors with Chemical Analysis 6.2 Basic Concepts of Neural Networks 6.3 Learning in Artificial Neural Networks (ANN) 6.4 Developing Neural Network–Based Systems 6.5 A Sample Neural Network Project 6.6 Other Neural Network Paradigms 6.7 Applications of Artificial Neural Networks 6.8 A Neural Network Software Demonstration 6.1 OPENING VIGNETTE: USING NEURAL NETWORKS TO PREDICT BEER FLAVORS WITH CHEMICAL ANALYSIS Coors Brewers Ltd., based in Burton-upon-Trent, Britain’s brewing capital, is proud of having the United Kingdom’s top beer brands...
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...opportunity of big data.” — Craig Vaughan Global Vice President at SAP “This timely book says out loud what has finally become apparent: in the modern world, Data is Business, and you can no longer think business without thinking data. Read this book and you will understand the Science behind thinking data.” — Ron Bekkerman Chief Data Officer at Carmel Ventures “A great book for business managers who lead or interact with data scientists, who wish to better understand the principals and algorithms available without the technical details of single-disciplinary books.” — Ronny Kohavi Partner Architect at Microsoft Online Services Division “Provost and Fawcett have distilled their mastery of both the art and science of real-world data analysis into an unrivalled introduction to the field.” —Geoff Webb Editor-in-Chief of Data Mining and Knowledge Discovery Journal “I would love it if everyone I had to work with had read this book.” — Claudia Perlich Chief Scientist of M6D (Media6Degrees) and Advertising Research Foundation Innovation Award Grand Winner (2013) www.it-ebooks.info “A foundational piece in the fast developing world of Data Science. A must read for anyone interested in the Big Data revolution." —Justin Gapper Business Unit Analytics Manager at Teledyne Scientific and Imaging “The authors, both renowned experts in data science before it had a name, have taken a complex topic and made it accessible to all levels, but mostly helpful to the budding data scientist. As far...
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...DATABASE MODELING AND DESIGN The Morgan Kaufmann Series in Data Management Systems (Selected Titles) Joe Celko’s Data, Measurements and Standards in SQL Joe Celko Information Modeling and Relational Databases, 2nd Edition Terry Halpin, Tony Morgan Joe Celko’s Thinking in Sets Joe Celko Business Metadata Bill Inmon, Bonnie O’Neil, Lowell Fryman Unleashing Web 2.0 Gottfried Vossen, Stephan Hagemann Enterprise Knowledge Management David Loshin Business Process Change, 2nd Edition Paul Harmon IT Manager’s Handbook, 2nd Edition Bill Holtsnider & Brian Jaffe Joe Celko’s Puzzles and Answers, 2 Joe Celko nd Location-Based Services ` Jochen Schiller and Agnes Voisard Managing Time in Relational Databases: How to Design, Update and Query Temporal Data Tom Johnston and Randall Weis Database Modeling with MicrosoftW Visio for Enterprise Architects Terry Halpin, Ken Evans, Patrick Hallock, Bill Maclean Designing Data-Intensive Web Applications Stephano Ceri, Piero Fraternali, Aldo Bongio, Marco Brambilla, Sara Comai, Maristella Matera Mining the Web: Discovering Knowledge from Hypertext Data Soumen Chakrabarti Advanced SQL: 1999—Understanding Object-Relational and Other Advanced Features Jim Melton Database Tuning: Principles, Experiments, and Troubleshooting Techniques Dennis Shasha, Philippe Bonnet SQL: 1999—Understanding Relational Language Components Jim Melton, Alan R. Simon Information Visualization in Data Mining and Knowledge Discovery Edited by Usama Fayyad, Georges G. Grinstein...
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...Deep Learning more at http://ml.memect.com Contents 1 Artificial neural network 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2.1 Improvements since 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.1 Network function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.2 Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.3 Learning paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.4 Learning algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Employing artificial neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.1 Real-life applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.2 Neural networks and neuroscience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.6 Neural network software ...
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...Lecture Notes in Computer Science Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen 6336 Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Alfred Kobsa University of California, Irvine, CA, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Germany Madhu Sudan Microsoft Research, Cambridge, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbruecken, Germany Richard Hull Jan Mendling Stefan Tai (Eds.) Business Process Management 8th International Conference, BPM 2010 Hoboken, NJ, USA, September 13-16, 2010 Proceedings 13 Volume Editors Richard Hull IBM Research, Thomas J. Watson Research Center 19 Skyline Drive, Hawthorne, NY 10532, USA E-mail: hull@us.ibm.com Jan Mendling Humboldt-Universität zu Berlin, Institut für Wirtschaftsinformatik Unter den Linden 6, 10099 Berlin, Germany E-mail: contact@mendling.com Stefan Tai Karlsruhe Institute of...
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...500 extraordinary islands G R E E N L A N D Beaufort Sea Baffin Bay vi Da i tra sS t a nm De it Stra rk Hudson Bay Gulf of Alaska Vancouver Portland C A N A D A Calgary Winnipeg Newfoundland Quebec Minneapolis UNITED STATES San Francisco Los Angeles San Diego Phoenix Dallas Ottawa Montreal ChicagoDetroitToronto Boston New York OF AMERICA Philadelphia Washington DC St. Louis Atlanta New Orleans Houston Monterrey NORTH AT L A N T I C OCEAN MEXICO Guadalajara Mexico City Gulf of Mexico Miami Havana CUBA GUATEMALA HONDURAS b e a n Sea EL SALVADOR NICARAGUA Managua BAHAMAS DOMINICAN REPUBLIC JAMAICA San Juan HAITI BELIZE C a r PUERTO RICO ib TRINIDAD & Caracas N TOBAGO A COSTA RICA IA M PANAMA VENEZUELA UYANRINA H GU C U G Medellín A PAC I F I C OCEAN Galapagos Islands COLOMBIA ECUADOR Bogotá Cali S FR EN Belém Recife Lima BR A Z I L PERU La Paz Brasélia Salvador Belo Horizonte Rio de Janeiro ~ Sao Paulo BOLIVIA PARAGUAY CHILE Cordoba Santiago Pôrto Alegre URUGUAY Montevideo Buenos Aires ARGENTINA FALKLAND/MALVINAS ISLANDS South Georgia extraordinary islands 1st Edition 500 By Julie Duchaine, Holly Hughes, Alexis Lipsitz Flippin, and Sylvie Murphy Contents Chapter 1 Beachcomber Islands . . . . . . . . . . . . . . . 1 Aquatic Playgrounds 2 Island Hopping the Turks & Caicos: Barefoot Luxury 12 Life’s a Beach 14 Unvarnished & Unspoiled 21 Sailing...
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