Abstract:
Pattern extraction has been done in past to extract hidden and interesting patterns
from large datasets. Recently, advancements are being made in these techniques by providing
the ability of multi-level mining, effective dimension reduction, advanced evaluation and
visualization support. This paper focuses on reviewing the current techniques in literature on
the basis of these parameters. Literature review suggests that most of the techniques which
provide multi-level mining and dimension reduction, do not handle mixed-type data during the
process. Patterns are not extracted using advanced algorithms for large datasets. Moreover, the
evaluation of patterns is not done using advanced measures which are suited for highdimensional
data. Techniques which provide visualization support are unable to handle large
number of rules in a small space. We present a theoretical model to handle these issues. The
implementation of the model is beyond the scope of this paper.
