Extracting Knowledge from Life Courses: Clustering and Visualization
نویسندگان
چکیده
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, nevertheless, be of interest for other kind of sequential data such as DNA analysis or web logs.
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