Frequent pattern mining is an essential theme in data mining. Existing algorithms usually
use a bottom-up search strategy. However, for very high dimensional data, this strategy
cannot fully utilize the minimum support constraint to prune the rowset search space. In this
paper, we propose a new method called top-down mining together with a novel row
enumeration tree to make full use of the pruning power of the minimum support constraint.
Furthermore, to efficiently check if a rowset is closed, we develop a method called the trace …

Many real world applications deal with transactional data, characterized by a huge number
of transactions (tuples) with a small number of dimensions (attributes). However, there are
some other applications that involve rather high dimensional data with a small number of
tuples. Examples of such applications include bioinformatics, survey-based statistical
analysis, text processing, and so on. High dimensional data pose great challenges to most
existing data mining algorithms. Although there are numerous algorithms dealing with …