Advertisement

Business and Consumer Analytics: New Ideas

  • Pablo Moscato
  • Natalie Jane de Vries

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Pablo Moscato, Natalie Jane de Vries
      Pages 3-117
    3. Natalie Jane de Vries, Pablo Moscato
      Pages 119-162
  3. Segmentation, Clustering and Pattern Mining

    1. Front Matter
      Pages 163-163
    2. Natalie Jane de Vries, Łukasz P. Olech, Pablo Moscato
      Pages 165-212
    3. Luke Mathieson, Pablo Moscato
      Pages 213-233
    4. Natalie Jane de Vries, Jamie Carlson, Pablo Moscato
      Pages 235-267
    5. Massimo Cafaro, Marco Pulimeno
      Pages 269-304
  4. Networks and Community Detection

    1. Front Matter
      Pages 305-305
    2. Ademir Gabardo, Regina Berretta, Pablo Moscato
      Pages 435-466
    3. James Abello, Daniel Mawhirter, Kevin Sun
      Pages 467-490
    4. Antonia Godoy-Lorite, Roger Guimerà, Marta Sales-Pardo
      Pages 491-512
    5. Luke Mathieson, Natalie Jane de Vries, Pablo Moscato
      Pages 513-541
  5. Memetic Algorithms

    1. Front Matter
      Pages 543-543
    2. Dimitra Trachanatzi, Eleftherios Tsakirakis, Magdalene Marinaki, Yannis Marinakis, Nikolaos Matsatsinis
      Pages 609-635
    3. Benjamin Biesinger, Bin Hu, Günther R. Raidl
      Pages 637-660
    4. Claudio Sanhueza Lobos, Natalie Jane de Vries, Mario Inostroza-Ponta, Regina Berretta, Pablo Moscato
      Pages 661-689
  6. Meta-Analytics

    1. Front Matter
      Pages 691-691
    2. Ringolf Thomschke, Stefan Voß, Stefan Lessmann
      Pages 733-779
    3. Mohammad Nazmul Haque, Natalie Jane de Vries, Pablo Moscato
      Pages 781-813
  7. Data Science Applications in Travel and Fashion Analytics

    1. Front Matter
      Pages 837-837
    2. Pierpaolo D’Urso, Marta Disegna, Riccardo Massari
      Pages 839-863
    3. Mustafa Mısır, Hoong Chuin Lau
      Pages 865-909
    4. Joseph Andria, Giacomo di Tollo, Raffaele Pesenti
      Pages 911-932
    5. Heri Ramampiaro, Helge Langseth, Thomas Almenningen, Herman Schistad, Martin Havig, Hai Thanh Nguyen
      Pages 933-961
  8. Appendix

    1. Front Matter
      Pages 963-963
    2. Natalie Jane de Vries, Pablo Moscato
      Pages 965-987
  9. Back Matter
    Pages 989-1005

About this book

Introduction

This two-volume handbook presents a collection of novel methodologies with applications and illustrative examples in the areas of data-driven computational social sciences. Throughout this handbook, the focus is kept specifically on business and consumer-oriented applications with interesting sections ranging from clustering and network analysis, meta-analytics, memetic algorithms, machine learning, recommender systems methodologies, parallel pattern mining and data mining to specific applications in market segmentation, travel, fashion or entertainment analytics. A must-read for anyone in data-analytics, marketing, behavior modelling and computational social science, interested in the latest applications of new computer science methodologies.

The chapters are contributed by leading experts in the associated fields.The chapters cover technical aspects at different levels, some of which are introductory and could be used for teaching. Some chapters aim at building a common understanding of the methodologies and recent application areas including the introduction of new theoretical results in the complexity of core problems.  Business and marketing professionals may use the book to familiarize themselves with some important foundations of data science. The work is a good starting point to establish an open dialogue of communication between professionals and researchers from different fields.

Together, the two volumes present a number of different new directions in Business and Customer Analytics with an emphasis in personalization of services, the development of new mathematical models and new algorithms, heuristics and metaheuristics applied to the challenging problems in the field. Sections of the book have introductory material to more specific and advanced themes in some of the chapters, allowing the volumes to be used as an advanced textbook. Clustering, Proximity Graphs, Pattern Mining, Frequent Itemset Mining, Feature Engineering, Network and Community Detection, Network-based Recommending Systems and Visualization, are some of the topics in the first volume. Techniques on Memetic Algorithms and their applications to Business Analytics and Data Science are surveyed in the second volume; applications in Team Orienteering, Competitive Facility-location, and Visualization of Products and Consumers are also discussed. The second volume also includes an introduction to Meta-Analytics, and to the application areas of Fashion and Travel Analytics. Overall, the two-volume set helps to describe some fundamentals, acts as a bridge between different disciplines, and presents important results in a rapidly moving field combining powerful optimization techniques allied to new mathematical models critical for personalization of services. 

Academics and professionals working in the area of business anyalytics, data science, operations research and marketing will find this handbook valuable as a reference. Students studying  these fields will find this handbook useful and helpful as a secondary textbook.


Keywords

Customer analytics Data science Business analytics Heuristics Memetic algorithms Network analysis Data mining Graph analytics metaheuristics memetic algorithms evolutionary algorithms heuristics mathematical modeling Meta-analytics Fashion and Travel Analytics

Editors and affiliations

  • Pablo Moscato
    • 1
  • Natalie Jane de Vries
    • 2
  1. 1.School of Electrical Engineering and ComputingThe University of NewcastleCallaghanAustralia
  2. 2.School of Electrical Engineering and ComputingThe University of NewcastleCallaghanAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-06222-4
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-06221-7
  • Online ISBN 978-3-030-06222-4
  • Buy this book on publisher's site