document: Aggregating time partitions Partitions of sequential data exist either per se or as a result of sequence segmentation algorithms . It is often the case that the same timeline is partitioned in many different ways . For example , different segmentation algorithms produce different partitions of the same underlying data points . In such cases , we are interested in producing an aggregate partition , i.e. , a segmentation that agrees as much as possible with the input segmentations . Each partition is defined as a set of continuous non-overlapping segments of the timeline . We show that this problem can be solved optimally in polynomial time using dynamic programming . We also propose faster greedy heuristics that work well in practice . We experiment with our algorithms and we demonstrate their utility in clustering the behavior of mobile-phone users and combining the results of different segmentation algorithms on genomic sequences .
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