Bruno Pimentel
ABSTRACT
To solve the problem of situations with nonlinearly separable clusters, kernel clustering methods have been proposed. Symbolic Data Analysis (SDA) has emerged to deal with variables that can have intervals, histograms, and even functions as values, in order to consider the variability and/or uncertainty innate to the data. In this paper, we present a K-means clustering method based in kernelized squared L2 distance for symbolic interval-type data. Experiments with real and syntectic symbolic interval-type data sets are considered.
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Index Terms
- A partitioning method for symbolic interval data based on kernelized metric
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