| 000 | 06797nam a22005295i 4500 | ||
|---|---|---|---|
| 001 | 978-3-030-06222-4 | ||
| 003 | DE-He213 | ||
| 005 | 20240423125134.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 190530s2019 sz | s |||| 0|eng d | ||
| 020 |
_a9783030062224 _9978-3-030-06222-4 |
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| 024 | 7 |
_a10.1007/978-3-030-06222-4 _2doi |
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| 050 | 4 | _aTK5105.5-5105.9 | |
| 072 | 7 |
_aUKN _2bicssc |
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| 072 | 7 |
_aCOM043000 _2bisacsh |
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| 072 | 7 |
_aUKN _2thema |
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| 082 | 0 | 4 |
_a004.6 _223 |
| 245 | 1 | 0 |
_aBusiness and Consumer Analytics: New Ideas _h[electronic resource] / _cedited by Pablo Moscato, Natalie Jane de Vries. |
| 250 | _a1st ed. 2019. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
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| 300 |
_aXVIII, 1005 p. 210 illus., 167 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _a1 Marketing meets Data Science: Bridging the gap -- 2 Consumer behaviour and marketing fundamentals for business data analytics -- 3 Introducing Clustering with a focus in Marketing and Consumer Analytics -- 4 An Introduction to Proximity Graphs -- 5 Clustering consumers and cluster-specific behavioural models -- 6 Frequent Itemset Mining -- 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 Identify Consumer Behaviour Modeling Constructs -- 13 Memetic Algorithms for Business Analytics and Data Science: A Brief Survey -- 14 A Memetic Algorithm for the Team Orienteering Problem -- 15 A Memetic Algorithm for Competitive Facility Location Problems -- 15 Visualizing Products and Consumers: A Gestalt Theory inspired method -- 16 Visualizing Products and Consumers: A Gestalt Theory inspired method -- 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 Classifier Ensembles -- 20 A Multi-objective Meta-Analytic Method for Customer Churn Prediction -- 21 Hotel classification using meta-analytics: a case study with cohesive clustering -- 22 Fuzzy clustering in travel and tourism analytics -- 23 Towards Personalized Data-Driven Bundle Design with QoS Constraint -- 24 A fuzzy evaluation of tourism sustainability -- 25 New Ideas in ranking for Personalised Fashion Recommender Systems -- 26 Datasets for Business and Consumer Analytics -- . | |
| 520 | _aThis 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 commonunderstanding 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. | ||
| 650 | 0 | _aComputer networks . | |
| 650 | 0 | _aBusiness information services. | |
| 650 | 0 |
_aComputer science _xMathematics. |
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| 650 | 0 | _aDiscrete mathematics. | |
| 650 | 1 | 4 | _aComputer Communication Networks. |
| 650 | 2 | 4 | _aBusiness Information Systems. |
| 650 | 2 | 4 | _aDiscrete Mathematics in Computer Science. |
| 700 | 1 |
_aMoscato, Pablo. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 700 | 1 |
_ade Vries, Natalie Jane. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030062217 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030062231 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-06222-4 |
| 912 | _aZDB-2-SCS | ||
| 912 | _aZDB-2-SXCS | ||
| 942 | _cSPRINGER | ||
| 999 |
_c174676 _d174676 |
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