Hierarchical, prototype-based clustering of multiple time series with missing values
Pekka Wartiainen, Tommi Kärkkäinen · 2015
A novel technique based on a robust clustering algorithm and multiple internal cluster indices is proposed. The suggested, hierarchical approach allows one to generate a dynamic decision tree like structure to represent the original data in the leaf nodes. It is applied here to divide a given set of multiple time series containing missing values into disjoint subsets. The whole algorithm is first described and then experimented with one particular data set from the UCI repository, already used in (1) for a similar exploration. The obtained results are very promising.