Courseware usage archetyping

2017

Data Min Knowl Disc

We intend to understand the growing amount of sports performance data by finding extreme data points, which makes human interpretation easier. In archetypoid analysis each datum is expressed as a mixture of actual observations (archetypoids). Therefore, it allows us to identify not only extreme athletes and teams, but also the composition of other athletes (or teams) according to the archetypoid athletes, and to establish a ranking. The utility of archetypoids in sports is illustrated with basketball and soccer data in three scenarios. Firstly, with multivariate data, where they are compared with other alternatives, showing their best results. Secondly, despite the fact that functional data are common in sports (time series or trajectories), functional data analysis has not been exploited until now, due to the sparseness of functions. In the second scenario, we extend archetypoid analysis for sparse functional data, furthermore showing the potential of functional data analysis in sports analytics. Finally, in the third scenario, features are not available, so we use proximities. We extend archetypoid analysis when asymmetric relations are present in data. This study provides information that will provide valuable knowledge about player/team/league performance so that we can analyze athlete's careers.

Section: Related Workmentioning

confidence: 99%

Archetypoid analysis for sports analytics

Vinué

2017

Data Min Knowl Disc

“…AA and ADA have therefore aroused the interest of researchers working in different fields, such as astrophysics (Chan et al (2003)), biology (D'Esposito et al (2012)), climate (Steinschneider and Lall (2015); Su et al (2017); Epifanio et al (2018a)), developmental psychology (Ragozini et al (2017)), e-learning (Theodosiou et al (2013)), engineering (Epifanio et al (2013(Epifanio et al ( , 2018b; Millán-Roures et al (2018)), genetics (Thøgersen et al (2013)), machine learning (Mørup and Hansen (2012); Seth and Eugster (2016a,b); Ragozini and D'Esposito (2015)), multi-document summarization Kononenko (2013, 2014)), nanotechnology (Fernandez and Barnard (2015)), neuroscience (Tsanousa et al (2015); Hinrich et al (2016)) and sports (Eugster (2012)). AA has also been applied in market research (Li et al (2003); Porzio et al (2008); Midgley and Venaik (2013)) in the multivariate context.…”

Section: Introductionmentioning

confidence: 99%

Robust multivariate and functional archetypal analysis with application to financial time series analysis

Moliner

Physica A: Statistical Mechanics and its Applications

2019

Archetypal analysis approximates data by means of mixtures of actual extreme cases (archetypoids) or archetypes, which are a convex combination of cases in the data set. Archetypes lie on the boundary of the convex hull. This makes the analysis very sensitive to outliers. A robust methodology by means of M-estimators for classical multivariate and functional data is proposed. This unsupervised methodology allows complex data to be understood even by non-experts. The performance of the new procedure is assessed in a simulation study, where a comparison with a previous methodology for the multivariate case is also carried out, and our proposal obtains favorable results. Finally, robust bivariate functional archetypoid analysis is applied to a set of companies in the S&P 500 described by two time series of stock quotes. A new graphic representation is also proposed to visualize the results. The analysis shows how the information can be easily interpreted and how even non-experts can gain a qualitative understanding of the data.

“…AA and ADA have been applied in many different fields, such as astrophysics (Chan et al (2003)), biology (D'Esposito et al (2012)), developmental psychology (Ragozini et al (2017)), e-learning (Theodosiou et al (2013)), genetics (Thøgersen et al (2013)), global development (Epifanio (2016)), industrial engineering (Epifanio et al (2013); Vinué et al (2015a)), machine learning problems (Mørup and Hansen (2012)), market research (Li et al (2003); Porzio et al (2008); Midgley and Venaik (2013)), multi-document summarization Kononenko (2013, 2014)), neuroscience (Tsanousa et al (2015); Hinrich et al (2016)) and sports (Eugster (2012); Vinué and Epifanio (2017)). AA and ADA algorithms are implemented in the R package archetypes (Eugster and Leisch (2009)) and Anthropometry (Vinué et al (2015b); Vinué (2017)), respectively.…”

Section: Introductionmentioning

confidence: 99%

Archetypal shapes based on landmarks and extension to handle missing data

Ibáñez

Simó

2017

Adv Data Anal Classif

Archetype and archetypoid analysis are extended to shapes. The objective is to find representative shapes. Archetypal shapes are pure (extreme) shapes. We focus on the case where the shape of an object is represented by a configuration matrix of landmarks. As shape space is not a vectorial space, we work in the tangent space, the linearized space about the mean shape. Then, each observation is approximated by a convex combination of actual observations (archetypoids) or archetypes, which are a convex combination of observations in the data set. These tools can contribute to the understanding of shapes, as in the usual multivariate case, since they lie somewhere between clustering and matrix factorization methods. A new simplex visualization tool is also proposed to provide a picture of the archetypal analysis results. We also propose new algorithms for performing archetypal analysis with missing data and its extension to incomplete shapes. A well-known data set is used to illustrate the methodologies developed. The proposed methodology is applied to an apparel design problem in children.