MDL-based Segmentation of Multi-Attribute Sequences
Many real-life multi-attribute sequences (multi-sequences)have a segmental structure, with segments of differing structures of attribute dependencies, that reflect an evolving nature of the dependencies over time and space.We propose a new approach for discovering a segmental structure of such evolving dependencies in probabilistic terms as a sequence of Dynamic Bayesian Networks (DBN). We use the Minimum Description Length(MDL)Principle to partition the multisequence into non-overlapping and homogeneous segments by fitting an optimal sequence of DBNs to the multi-sequence. In experiments, conducted on daily rainfall data we showed the applicability of the method for discovering interesting spatiotemporal evolving dependencies between rainfall occurrences in south-western Australia.
exploratory spatial data analysis and/or mining partial random walk evolutionary spectrum test general kriging method EM algorithm
Robert Gwadera
IBM Zurich Research Laboratory
国际会议
福州
英文
106-111
2011-06-29（万方平台首次上网日期，不代表论文的发表时间）