Business and Consumer Analytics: New Ideas
by: Pablo Moscato
ISBN-10: 303006221X
ISBN-13: 9783030062217
Edition 版次: 1st ed. 2019
Publication Date 出版日期: 2019-06-13
Print Length 页数: 1005 pages
Book Description
By finelybook
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.
Part 1. Introduction
1. Marketing Meets Data Science: Bridging the Gap
2. Consumer Behaviour and Marketing Fundamentals for Business Data Analytics
Part ll. Segmentation,Clustering and Pattern Mining
3. Introducing Clustering with a Focus in Marketing and Consumer Analysis
4. An Introduction to Proximity Graphs
5. Clustering Consumers and Cluster-Specific Behavioural Models
6. Frequent ltemset Mining
Part ll. Networks and Community Detection
7. Business Network Analytics: From Graphs to Supernetworks
8. Centrality in Networks: Finding the Most Important Nodes
9. Overlapping Communities in Co-purchasing and Social Interaction Graphs: A Memetic Approach
10. Taming a Graph Hairball: Local Exploration in a Global Context
11. Network-Based Models for Social Recommender Systems
12. Using Network Alignment to ldentify Conserved Consumer Behaviour Modelling Constructs
Part V. Memetic Algorithms
13. Memetic Algorithms for Business Analytics and Data Science: A Brief Survey
14.A Memetic Algorithm for the Team Orienteering Problem
15. AMemetic Algorithm for Competitive Facility Location Problems
16. Visualizing Products and Consumers: A Gestalt Theory Inspired Method
Part V. Meta-Analytics
17. An Overview of Meta-Analytics: The Promise of Unifying Metaheuristics and Analytics
18. From Ensemble Learning to Meta-Analytics: A Review on Trends in Business Applications
19. Metaheuristics and Classifher Ensembles
20.A Multi-objective Meta-Analytic Method for Customer Churn Prediction
21. Hotel Classification Using Meta-Analytics: A Case Study with Cohesive Clustering
Part 1. Data Science Applications in Travel and Fashion Analytics
22. Fuzzy Clustering in Travel and Tourism Analytics
23. Towards Personalized Data-Driven Bundle Design with QoS Constraint
24.A Fuzy Evaluation of Tourism Sustainability
25. New ldeas in Ranking for Personalized Fashion Recommender Systems
Part Vl. Appendix
26. Datasets for Business and Consumer Analytics
