PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Aggregating Time Partitions
Taneli Mielikäinen, Evimaria Terzi and Panayiotis Tsaparas
In: KDD 2006, 20-23 Aug 2006, Philadelphia, PA, USA.

Abstract

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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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:2131
Deposited By:Taneli Mielikäinen
Deposited On:19 July 2006