Model‐based Clustering and Classification for Data Science, CharlesBouveyron, GillesCeleux, T. BrendanMurphy and Adrian E.Raftery, Cambridge University Press, 2019, 427 + xvii pages, £59.99, hardcover, ISBN: 978‐1‐1084‐9420‐5

2020 ◽  
Vol 88 (1) ◽  
pp. 263-264
Author(s):  
Antony Unwin
Keyword(s):  
Data Science ◽  
Cambridge University ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

scholarly journals Special issue on “Advances on model-based clustering and classification”

2019 ◽  
Vol 13 (1) ◽  
pp. 1-5 ◽  
Author(s):  
Sylvia Frühwirth-Schnatter ◽  
Salvatore Ingrassia ◽  
Agustín Mayo-Iscar
Keyword(s):  
Special Issue ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

Clustering Methods in Game Data Science

2021 ◽  
pp. 133-178
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Hierarchical Clustering ◽  
Data Science ◽  
The Other ◽  
Clustering Methods ◽  
Clustering Techniques ◽  
Model Based Clustering ◽  
Fuzzy C Means ◽  
Model Based

This chapter discusses different clustering methods and their application to game data. In particular, the chapter details K-means, Fuzzy C-Means, Hierarchical Clustering, Archetypical Analysis, and Model-based clustering techniques. It discusses the disadvantages and advantages of the different methods and discusses when you may use one method vs. the other. It also identifies and shows you ways to visualize the results to make sense of the resulting clusters. It also includes details on how one would evaluate such clusters or go about applying the algorithms to a game dataset. The chapter includes labs to delve deeper into the application of these algorithms on real game data.

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