  Data mining has been widely recognized as a powerful tool to explore added
value from large-scale databases. Finding frequent item sets in databases is a
crucial in data mining process of extracting association rules. Many algorithms
were developed to find the frequent item sets. This paper presents a summary
and a comparative study of the available FP-growth algorithm variations
produced for mining frequent item sets showing their capabilities and
efficiency in terms of time and memory consumption on association rule mining
by taking application of specific information into account. It proposes pattern
growth mining paradigm based FP-tree growth algorithm, which employs a tree
structure to compress the database. The performance study shows that the anti-
FP-growth method is efficient and scalable for mining both long and short
frequent patterns and is about an order of magnitude faster than the Apriority
algorithm and also faster than some recently reported new frequent-pattern
mining.
