Clustered segmentations

The problem of sequence and time-series segmentation has been discussed widely and it has been applied successfully in a variety of areas, including computational genomics, data analysis for scientific applications, and telecommunications. In many of these areas the sequences involved are multidimensional, and the goal of the segmentation is to discover sequence segments with small variability. One of the characteristics of existing techniques is that they force all dimensions to share the same segment boundaries, yet, it is often reasonable to assume that different dimensions are more correlated than others, and that concrete and meaningful states are associated only with a subset of dimensions. In this paper we study the problem of segmenting a multidimensional sequence when the dimensions of the sequence are allowed to form clusters and be segmented separately within each cluster. We demonstrate the relevance of this problem to many data-mining applications. We discuss the connection of our setting with existing work, we show the hardness of the suggested problem, and we propose a number of algorithms for its solution. Finally, we give empirical evidence showing that our algorithms work well in practice and produce useful results.