Parallel and distributed frequent-regular patternmining using vertical format in large databases

Abstract

A good number of parallel and distributed frequent pattern mining algorithms have been proposed so far for the large and/or distributed databases. Not only occurrence frequency of a pattern but also occurrence behavior (regularity) of a pattern may be treated as an emerging area in data mining research. So far some efforts have been made to mine regular patterns but there is no suitable algorithm exists to mine frequent-regular patterns in parallel and distributed environment. Therefore, in this paper we introduced a new method called PFRP-method (Parallel Frequent Regular Pattern-method) to discover frequent-regular patterns in large databases using vertical data format which requires only one database scan. Our method works in parallel at each local site in order to reduce I/O cost and inter-process communication, generates all frequent-regular patterns in the final phase. Our experiment results show that our PFRP-method is highly efficient in large databases.