Model-Based Clustering
This chapter deals with the model-based approach to clustering based on a probability model, specifically the Finite Mixture Model (FMM). Within such a framework, the crucial problems of determining the number of clusters and choosing an appropriate clust
- PDF / 9,363,610 Bytes
- 346 Pages / 453.543 x 683.15 pts Page_size
- 72 Downloads / 200 Views
Paolo Giordani Maria Brigida Ferraro Francesca Martella
An Introduction to Clustering with R
Behaviormetrics: Quantitative Approaches to Human Behavior Volume 1
Series Editor Akinori Okada, Professor Emeritus, Rikkyo University, Tokyo, Japan
This series covers in their entirety the elements of behaviormetrics, a term that encompasses all quantitative approaches of research to disclose and understand human behavior in the broadest sense. The term includes the concept, theory, model, algorithm, method, and application of quantitative approaches from theoretical or conceptual studies to empirical or practical application studies to comprehend human behavior. The Behaviormetrics series deals with a wide range of topics of data analysis and of developing new models, algorithms, and methods to analyze these data. The characteristics featured in the series have four aspects. The first is the variety of the methods utilized in data analysis and a newly developed method that includes not only standard or general statistical methods or psychometric methods traditionally used in data analysis, but also includes cluster analysis, multidimensional scaling, machine learning, corresponding analysis, biplot, network analysis and graph theory, conjoint measurement, biclustering, visualization, and data and web mining. The second aspect is the variety of types of data including ranking, categorical, preference, functional, angle, contextual, nominal, multi-mode multi-way, contextual, continuous, discrete, high-dimensional, and sparse data. The third comprises the varied procedures by which the data are collected: by survey, experiment, sensor devices, and purchase records, and other means. The fourth aspect of the Behaviormetrics series is the diversity of fields from which the data are derived, including marketing and consumer behavior, sociology, psychology, education, archaeology, medicine, economics, political and policy science, cognitive science, public administration, pharmacy, engineering, urban planning, agriculture and forestry science, and brain science. In essence, the purpose of this series is to describe the new horizons opening up in behaviormetrics — approaches to understanding and disclosing human behaviors both in the analyses of diverse data by a wide range of methods and in the development of new methods to analyze these data. Editor in Chief Akinori Okada (Rikkyo University) Managing Editors Daniel Baier (University of Bayreuth) Giuseppe Bove (Roma Tre University) Takahiro Hoshino (Keio University)
More information about this series at http://www.springer.com/series/16001
Paolo Giordani Maria Brigida Ferraro Francesca Martella •
•
An Introduction to Clustering with R
123
Paolo Giordani Dipartimento di Scienze Statistiche Sapienza Università di Roma Rome, Italy
Maria Brigida Ferraro Dipartimento di Scienze Statistiche Sapienza Università di Roma Rome, Italy
Francesca Martella Dipartimento di Scienze Statistiche Sapienza Università di Roma Rome, Italy
ISSN 2524-4027 ISSN 2524-4035 (electronic) Behav
Data Loading...
