Abstract
This article presents some of the facilities offered by our TraMineR R-package for clustering and visualizing sequence data. Firstly, we discuss our implementation of the optimal matching algorithm for evaluating the distance between two sequences and its use for generating a distance matrix for the whole sequence data set. Once such a matrix is obtained, we may use it as input for a cluster analysis, which can be done straightforwardly with any method available in the R statistical environment. Then we present three kinds of plots for visualizing the characteristics of the obtained clusters: an aggregated plot depicting the average sequential behavior of cluster members; an sequence index plot that shows the diversity inside clusters and an original frequency plot that highlights the frequencies of the n most frequent sequences. TraMineR was designed for analysing sequences representing life courses and our presentation is illustrated on such a real world data set. The material presented should also be of interest for other kind of sequential data such as DNA analysis or web logs.
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Müller, N.S., Gabadinho, A., Ritschard, G., Studer, M. (2008). Extracting Knowledge from Life Courses: Clustering and Visualization. In: Song, IY., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2008. Lecture Notes in Computer Science, vol 5182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85836-2_17
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DOI: https://doi.org/10.1007/978-3-540-85836-2_17
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