After reading the chapter by Capri (2015) on manual data collection

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After reading the chapter by Capri (2015) on manual data collection. Answer the following questions:each ques in 500 words

What were the traditional methods of data collection in the transit system?

Why are the traditional methods insufficient in satisfying the requirement of data collection?

Give a synopsis of the case study and your thoughts regarding the requirements of the optimization and performance measurement requirements and the impact to expensive and labor-intensive nature.

In an APA7 formatted essay answer all questions above. There should be headings to each of the questions above as well. Ensure there are at least two-peer reviewed sources to support your work. The paper should be at least two pages of content (this does not include the cover page or reference page).ch 3 in textbook: Alternative Techniques

INTRODUCTION TO DATA MINING INTRODUCTION TO DATA MINING SECOND EDITION GLOBAL EDITION PANG-NING TAN Michigan State University MICHAEL STEINBACH University of Minnesota ANUJ KARPATNE University of Minnesota VIPIN KUMAR University of Minnesota 330 Hudson Street, NY NY 10013 Director, Portfolio Management: Engineering, Computer Science & Global Editions: Julian Partridge Specialist, Higher Ed Portfolio Management: Matt Goldstein Portfolio Management Assistant: Meghan Jacoby Acquisitions Editor, Global Edition: Sourabh Maheshwari Managing Content Producer: Scott Disanno Content Producer: Carole Snyder Senior Project Editor, Global Edition: K.K. Neelakantan Web Developer: Steve Wright Manager, Media Production, Global Edition: Vikram Kumar Rights and Permissions Manager: Ben Ferrini Manufacturing Buyer, Higher Ed, Lake Side Communications Inc (LSC): Maura Zaldivar-Garcia Senior Manufacturing Controller, Global Edition: Caterina Pellegrino Inventory Manager: Ann Lam Product Marketing Manager: Yvonne Vannatta Field Marketing Manager: Demetrius Hall Marketing Assistant: Jon Bryant Cover Designer: Lumina Datamatics Full-Service Project Management: Ramya Radhakrishnan, Integra Software Services Pearson Education Limited KAO Two KAO Park Harlow CM17 9NA United Kingdom and Associated Companies throughout the world Visit us on the World Wide Web at: www.pearsonglobaleditions.com c Pearson Education Limited, 2019 The rights of Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, and Vipin Kumar to be identi?ed as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988. Authorized adaptation from the United States edition, entitled Introduction to Data Mining, 2nd Edition, ISBN 978-0-13-312890-1 by Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, and Vipin c 2019. Kumar, published by Pearson Education All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without either the prior written permission of the publisher or a license permitting restricted copying in the United Kingdom issued by the Copyright Licensing Agency Ltd, Sa?ron House, 6–10 Kirby Street, London EC1N 8TS. All trademarks used herein are the property of their respective owners. The use of any trademark in this text does not vest in the author or publisher any trademark ownership rights in such trademarks, nor does the use of such trademarks imply any a?liation with or endorsement of this book by such owners. For information regarding permissions, request forms, and the appropriate contacts within the Pearson Education Global Rights and Permissions department, please visit www.pearsoned.com/permissions. This eBook is a standalone product and may or may not include all assets that were part of the print version. It also does not provide access to other Pearson digital products like MyLab and Mastering. The publisher reserves the right to remove any material in this eBook at any time. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 10: 0-273-76922-7 ISBN 13: 978-0-273-76922-4 eBook ISBN 13: 978-0-273-77532-4 eBook formatted by Integra Software Services. To our families ... Preface to the Second Edition Since the ?rst edition, roughly 12 years ago, much has changed in the ?eld of data analysis. The volume and variety of data being collected continues to increase, as has the rate (velocity) at which it is being collected and used to make decisions. Indeed, the term Big Data has been used to refer to the massive and diverse data sets now available. In addition, the term data science has been coined to describe an emerging area that applies tools and techniques from various ?elds, such as data mining, machine learning, statistics, and many others, to extract actionable insights from data, often big data. The growth in data has created numerous opportunities for all areas of data analysis. The most dramatic developments have been in the area of predictive modeling, across a wide range of application domains. For instance, recent advances in neural networks, known as deep learning, have shown impressive results in a number of challenging areas, such as image classi?cation, speech recognition, as well as text categorization and understanding. While not as dramatic, other areas, e.g., clustering, association analysis, and anomaly detection have also continued to advance. This new edition is in response to those advances. Overview As with the ?rst edition, the second edition of the book provides a comprehensive introduction to data mining and is designed to be accessible and useful to students, instructors, researchers, and professionals. Areas covered include data preprocessing, predictive modeling, association analysis, cluster analysis, anomaly detection, and avoiding false discoveries. The goal is to present fundamental concepts and algorithms for each topic, thus providing the reader with the necessary background for the application of data mining to real problems. As before, classi?cation, association analysis and cluster analysis, are each covered in a pair of chapters. The introductory chapter covers basic concepts, representative algorithms, and evaluation techniques, while the more following chapter discusses advanced concepts and algorithms. As before, our objective is to provide the reader with a sound understanding of the foundations of data mining, while still covering many important advanced 6 Preface to the Second Edition topics. Because of this approach, the book is useful both as a learning tool and as a reference. To help readers better understand the concepts that have been presented, we provide an extensive set of examples, ?gures, and exercises. The solutions to the original exercises, which are already circulating on the web, will be made public. The exercises are mostly unchanged from the last edition, with the exception of new exercises in the chapter on avoiding false discoveries. New exercises for the other chapters and their solutions will be available to instructors via the web. Bibliographic notes are included at the end of each chapter for readers who are interested in more advanced topics, historically important papers, and recent trends. These have also been signi?cantly updated. The book also contains a comprehensive subject and author index. What is New in the Second Edition? Some of the most signi?cant improvements in the text have been in the two chapters on classi?cation. The introductory chapter uses the decision tree classi?er for illustration, but the discussion on many topics—those that apply across all classi?cation approaches— has been greatly expanded and clari?ed, including topics such as over?tting, under?tting, the impact of training size, model complexity, model selection, and common pitfalls in model evaluation. Almost every section of the advanced classi?cation chapter has been signi?cantly updated. The material on Bayesian networks, support vector machines, and arti?cial neural networks has been signi?cantly expanded. We have added a separate section on deep networks to address the current developments in this area. The discussion of evaluation, which occurs in the section on imbalanced classes, has also been updated and improved. The changes in association analysis are more localized. We have completely reworked the section on the evaluation of association patterns (introductory chapter), as well as the sections on sequence and graph mining (advanced chapter). Changes to cluster analysis are also localized. The introductory chapter added the K-means initialization technique and an updated the discussion of cluster evaluation. The advanced clustering chapter adds a new section on spectral graph clustering. Anomaly detection has been greatly revised and expanded. Existing approaches—statistical, nearest neighbor/density-based, and clustering based—have been retained and updated, while new approaches have been added: reconstruction-based, one-class classi?cation, and informationtheoretic. The reconstruction-based approach is illustrated using autoencoder networks that are part of the deep learning paradigm. The data chapter has Preface to the Second Edition 7 been updated to include discussions of mutual information and kernel-based techniques. The last chapter, which discusses how to avoid false discoveries and produce valid results, is completely new, and is novel among other contemporary textbooks on data mining. It supplements the discussions in the other chapters with a discussion of the statistical concepts (statistical signi?cance, p-values, false discovery rate, permutation testing, etc.) relevant to avoiding spurious results, and then illustrates these concepts in the context of data mining techniques. This chapter addresses the increasing concern over the validity and reproducibility of results obtained from data analysis. The addition of this last chapter is a recognition of the importance of this topic and an acknowledgment that a deeper understanding of this area is needed for those analyzing data. The data exploration chapter has been deleted, as have the appendices, from the print edition of the book, but will remain available on the web. A new appendix provides a brief discussion of scalability in the context of big data. To the Instructor As a textbook, this book is suitable for a wide range of students at the advanced undergraduate or graduate level. Since students come to this subject with diverse backgrounds that may not include extensive knowledge of statistics or databases, our book requires minimal prerequisites. No database knowledge is needed, and we assume only a modest background in statistics or mathematics, although such a background will make for easier going in some sections. As before, the book, and more speci?cally, the chapters covering major data mining topics, are designed to be as self-contained as possible. Thus, the order in which topics can be covered is quite ?exible. The core material is covered in chapters 2 (data), 3 (classi?cation), 4 (association analysis), 5 (clustering), and 9 (anomaly detection). We recommend at least a cursory coverage of Chapter 10 (Avoiding False Discoveries) to instill in students some caution when interpreting the results of their data analysis. Although the introductory data chapter (2) should be covered ?rst, the basic classi?cation (3), association analysis (4), and clustering chapters (5), can be covered in any order. Because of the relationship of anomaly detection (9) to classi?cation (3) and clustering (5), these chapters should precede Chapter 9. Various topics can be selected from the advanced classi?cation, association analysis, and clustering chapters (6, 7, and 8, respectively) to ?t the schedule and interests of the instructor and students. We also advise that the lectures be augmented by projects or practical exercises in data mining. Although they 8 Preface to the Second Edition are time consuming, such hands-on assignments greatly enhance the value of the course. Support Materials Support materials available to all readers of this book are available on the book’s website. • • • • PowerPoint lecture slides Suggestions for student projects Data mining resources, such as algorithms and data sets Online tutorials that give step-by-step examples for selected data mining techniques described in the book using actual data sets and data analysis software Additional support materials, including solutions to exercises, are available only to instructors adopting this textbook for classroom use. Acknowledgments Many people contributed to the ?rst and second editions of the book. We begin by acknowledging our families to whom this book is dedicated. Without their patience and support, this project would have been impossible. We would like to thank the current and former students of our data mining groups at the University of Minnesota and Michigan State for their contributions. Eui-Hong (Sam) Han and Mahesh Joshi helped with the initial data mining classes. Some of the exercises and presentation slides that they created can be found in the book and its accompanying slides. Students in our data mining groups who provided comments on drafts of the book or who contributed in other ways include Shyam Boriah, Haibin Cheng, Varun Chandola, Eric Eilertson, Levent Erto?z, Jing Gao, Rohit Gupta, Sridhar Iyer, Jung-Eun Lee, Benjamin Mayer, Aysel Ozgur, Uygar Oztekin, Gaurav Pandey, Kashif Riaz, Jerry Scripps, Gyorgy Simon, Hui Xiong, Jieping Ye, and Pusheng Zhang. We would also like to thank the students of our data mining classes at the University of Minnesota and Michigan State University who worked with early drafts of the book and provided invaluable feedback. We speci?cally note the helpful suggestions of Bernardo Craemer, Ari?n Ruslim, Jamshid Vayghan, and Yu Wei. Joydeep Ghosh (University of Texas) and Sanjay Ranka (University of Florida) class tested early versions of the book. We also received many useful suggestions directly from the following UT students: Pankaj Adhikari, Rajiv Bhatia, Frederic Bosche, Arindam Chakraborty, Meghana Deodhar, Chris Everson, David Gardner, Saad Godil, Todd Hay, Clint Jones, Ajay Joshi, Preface to the Second Edition 9 Joonsoo Lee, Yue Luo, Anuj Nanavati, Tyler Olsen, Sunyoung Park, Aashish Phansalkar, Geo? Prewett, Michael Ryoo, Daryl Shannon, and Mei Yang. Ronald Kosto? (ONR) read an early version of the clustering chapter and o?ered numerous suggestions. George Karypis provided invaluable LATEX assistance in creating an author index. Irene Moulitsas also provided assistance with LATEX and reviewed some of the appendices. Musetta Steinbach was very helpful in ?nding errors in the ?gures. We would like to acknowledge our colleagues at the University of Minnesota and Michigan State who have helped create a positive environment for data mining research. They include Arindam Banerjee, Dan Boley, Joyce Chai, Anil Jain, Ravi Janardan, Rong Jin, George Karypis, Claudia Neuhauser, Haesun Park, William F. Punch, Gyo?rgy Simon, Shashi Shekhar, and Jaideep Srivastava. The collaborators on our many data mining projects, who also have our gratitude, include Ramesh Agrawal, Maneesh Bhargava, Steve Cannon, Alok Choudhary, Imme Ebert-Upho?, Auroop Ganguly, Piet C. de Groen, Fran Hill, Yongdae Kim, Steve Klooster, Kerry Long, Nihar Mahapatra, Rama Nemani, Nikunj Oza, Chris Potter, Lisiane Pruinelli, Nagiza Samatova, Jonathan Shapiro, Kevin Silverstein, Brian Van Ness, Bonnie Westra, Nevin Young, and Zhi-Li Zhang. The departments of Computer Science and Engineering at the University of Minnesota and Michigan State University provided computing resources and a supportive environment for this project. ARDA, ARL, ARO, DOE, NASA, NOAA, and NSF provided research support for Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, and Vipin Kumar. In particular, Kamal Abdali, Mitra Basu, Dick Brackney, Jagdish Chandra, Joe Coughlan, Michael Coyle, Stephen Davis, Frederica Darema, Richard Hirsch, Chandrika Kamath, Tsengdar Lee, Raju Namburu, N. Radhakrishnan, James Sidoran, Sylvia Spengler, Bhavani Thuraisingham, Walt Tiernin, Maria Zemankova, Aidong Zhang, and Xiaodong Zhang have been supportive of our research in data mining and high-performance computing. It was a pleasure working with the helpful sta? at Pearson Education. In particular, we would like to thank Matt Goldstein, Kathy Smith, Carole Snyder, and Joyce Wells. We would also like to thank George Nichols, who helped with the art work and Paul Anagnostopoulos, who provided LATEX support. We are grateful to the following Pearson reviewers: Leman Akoglu (Carnegie Mellon University), Chien-Chung Chan (University of Akron), Zhengxin Chen (University of Nebraska at Omaha), Chris Clifton (Purdue University), Joydeep Ghosh (University of Texas, Austin), Nazli Goharian (Illinois Institute of Technology), J. Michael Hardin (University of Alabama), Jingrui He (Arizona 10 Preface to the Second Edition State University), James Hearne (Western Washington University), Hillol Kargupta (University of Maryland, Baltimore County and Agnik, LLC), Eamonn Keogh (University of California-Riverside), Bing Liu (University of Illinois at Chicago), Mariofanna Milanova (University of Arkansas at Little Rock), Srinivasan Parthasarathy (Ohio State University), Zbigniew W. Ras (University of North Carolina at Charlotte), Xintao Wu (University of North Carolina at Charlotte), and Mohammed J. Zaki (Rensselaer Polytechnic Institute). Over the years since the ?rst edition, we have also received numerous comments from readers and students who have pointed out typos and various other issues. We are unable to mention these individuals by name, but their input is much appreciated and has been taken into account for the second edition. Acknowledgments for the Global Edition Pearson would like to thank and acknowledge Pramod Kumar Singh (Atal Bihari Vajpayee Indian Institute of Information Technology and Management) for contributing to the Global Edition, and Annappa (National Institute of Technology Surathkal), Komal Arora, and Soumen Mukherjee (RCC Institute of Technology) for reviewing the Global Edition. Contents Preface to the Second Edition 1 Introduction 1.1 What Is Data Mining? . . . . . . . . 1.2 Motivating Challenges . . . . . . . . 1.3 The Origins of Data Mining . . . . . 1.4 Data Mining Tasks . . . . . . . . . . 1.5 Scope and Organization of the Book 1.6 Bibliographic Notes . . . . . . . . . . 1.7 Exercises . . . . . . . . . . . . . . . 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Data 2.1 Types of Data . . . . . . . . . . . . . . . . . . . . . 2.1.1 Attributes and Measurement . . . . . . . . 2.1.2 Types of Data Sets . . . . . . . . . . . . . . 2.2 Data Quality . . . . . . . . . . . . . . . . . . . . . 2.2.1 Measurement and Data Collection Issues . . 2.2.2 Issues Related to Applications . . . . . . . 2.3 Data Preprocessing . . . . . . . . . . . . . . . . . . 2.3.1 Aggregation . . . . . . . . . . . . . . . . . . 2.3.2 Sampling . . . . . . . . . . . . . . . . . . . 2.3.3 Dimensionality Reduction . . . . . . . . . . 2.3.4 Feature Subset Selection . . . . . . . . . . . 2.3.5 Feature Creation . . . . . . . . . . . . . . . 2.3.6 Discretization and Binarization . . . . . . . 2.3.7 Variable Transformation . . . . . . . . . . . 2.4 Measures of Similarity and Dissimilarity . . . . . . 2.4.1 Basics . . . . . . . . . . . . . . . . . . . . . 2.4.2 Similarity and Dissimilarity between Simple 2.4.3 Dissimilarities between Data Objects . . . . 2.4.4 Similarities between Data Objects . . . . . . . . . . . . 21 24 25 27 29 33 35 41 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Attributes . . . . . . . . . . . . . . . 43 46 47 54 62 62 69 70 71 72 76 78 81 83 89 91 92 94 96 98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Contents 2.5 2.6 2.4.5 Examples of Proximity Measures . . . 2.4.6 Mutual Information . . . . . . . . . . 2.4.7 Kernel Functions* . . . . . . . . . . . 2.4.8 Bregman Divergence* . . . . . . . . . 2.4.9 Issues in Proximity Calculation . . . . 2.4.10 Selecting the Right Proximity Measure Bibliographic Notes . . . . . . . . . . . . . . . Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....