Extracting Knowledge from Life Courses: Clustering and Visualization

Müller, Nicolas S.; Gabadinho, Alexis; Ritschard, Gilbert; Studer, Matthias

doi:10.1007/978-3-540-85836-2_17

Cited by 17 publications

(17 citation statements)

References 10 publications

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“…From a more technical viewpoint, SA relies on a distance measure between sequences (or trajectories) of states, which allows for their comparison (Abbott and Forrest 1986;Elzinga 2005;Müller et al 2008). …”

Section: Sequence Analysis (Sa)mentioning

confidence: 99%

Estimating the Relationship between Time-varying Covariates and Trajectories: The Sequence Analysis Multistate Model Procedure

Studer

Struffolino

Fasang

2018

Sociological Methodology

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The relationship between processes and time-varying covariates is of central theoretical interest in addressing many social science research questions. On the one hand, event history analysis has been the chosen method to study these kinds of relationships when the outcomes can be meaningfully specified as simple instantaneous events or transitions. On the other hand, sequence analysis has made increasing inroads into the social sciences to analyze trajectories as holistic "process outcomes". We propose an original combination of these two approaches called Sequence Analysis Multistate Model (SAMM) procedure. The SAMM procedure allows studying the relationship between time-varying covariates and trajectories of categorical states specified as process outcomes that unfold over * Corresponding author. Email: matthias.studer@unige.ch.

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Section: Sequence Analysis (Sa)mentioning

confidence: 99%

Estimating the Relationship between Time-varying Covariates and Trajectories: The Sequence Analysis Multistate Model Procedure

Studer

Struffolino

Fasang

2018

Sociological Methodology

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“…Hence, we suppose that there are no insertions or deletions and that only the substitution costs have to be defined for OM metrics. Among optimal-matching dissimilarities, we selected three dissimilarities: the OM with substitution costs computed from the transition matrix between statuses as proposed in [64], the Hamming dissimilarity (HAM, no insertion or deletion costs and a substitution cost equal to 1) and the Dynamic Hamming dissimilarity (DHD as described in [65]). …”

Section: Multiple Relational Som On Real Datamentioning

confidence: 99%

On-line relational and multiple relational SOM

Olteanu¹,

Villa-Vialaneix²

2015

Neurocomputing

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In some applications and in order to address real-world situations better, data may be more complex than simple numerical vectors. In some examples, data can be known only through their pairwise dissimilarities or through multiple dissimilarities, each of them describing a particular feature of the data set. Several variants of the Self Organizing Map (SOM) algorithm were introduced to generalize the original algorithm to the framework of dissimilarity data. Whereas median SOM is based on a rough representation of the prototypes, relational SOM allows representing these prototypes by a virtual linear combination of all elements in the data set, referring to a pseudo-euclidean framework. In the present article, an on-line version of relational SOM is introduced and studied. Similarly to the situation in the Euclidean framework, this on-line algorithm provides a better organization and is much less sensible to prototype initialization than standard (batch) relational SOM. In a more general case, this stochastic version allows us to integrate an additional stochastic gradient descent step in the algorithm which can tune the respective weights of several dissimilarities in an optimal way: the resulting multiple relational SOM thus has the ability to integrate several sources of data of different types, or to make a consensus between several dissimilarities describing the same data. The algorithms introduced in this manuscript are tested on several data sets, including categorical data and graphs. On-line relational SOM is currently available in the R package SOMbrero that can be downloaded at http://sombrero.r-forge.r-project.org/ or directly tested on its Web User Interface at http://shiny.nathalievilla.org/sombrero.

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“…The data were first presented in Müller et al (2008). Briefly, these data represent family formation trajectories of Swiss individuals who were at least 30 years old at the time of the survey.…”

Section: An Application To Family Formation Data and Distancesmentioning

confidence: 99%

Spell Sequences, State Proximities, and Distance Metrics

Elzinga¹,

Studer

2014

Sociological Methods & Research

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Because optimal matching (OM) distance is not very sensitive to differences in the order of states, we introduce a subsequence-based distance measure that can be adapted to subsequence length, to subsequence duration, and to soft-matching of states. Using a simulation technique developed by Studer, we investigate the sensitivity, relative to OM, of several variants of this metric to variations in order, timing, and duration of states. The results show that the behavior of the metric is as intended. Furthermore, we use family formation data from the Swiss Household Panel to compare a few variants of the new metric to OM. The new metrics have been implemented in the freely available TraMineR-package.

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Extracting Knowledge from Life Courses: Clustering and Visualization

Cited by 17 publications

References 10 publications

Estimating the Relationship between Time-varying Covariates and Trajectories: The Sequence Analysis Multistate Model Procedure

Estimating the Relationship between Time-varying Covariates and Trajectories: The Sequence Analysis Multistate Model Procedure

On-line relational and multiple relational SOM

Spell Sequences, State Proximities, and Distance Metrics

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