Although symbolic data tables summarize huge sets of data they can still become very large in size. This paper proposes a method for compressing a symbolic data table using the recently emerged Compound Term Composition Algebra. One charisma of CTCA is that the closed world hypotheses of its operations can lead to a remarkably high 'compression ratio'. The compacted form apart from having much lower storage space requirements, it allows designing more e±cient algorithms for symbolic data analysis.
|Title of host publication||Proceedings of the Workshop on Symbolic and Spatial Data Analysis (SSDA) of ECML/PKDD 2004|
|Publication status||Published - 2004|