Abstract
Although symbolic data tables summarize huge sets of data they can still become very large in size. This paper proposes a novel technique for compressing a symbolic data table using the recently emerged Compound Term Composition Algebra. One advantage 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 efficient algorithms for symbolic data analysis.