Title: Two-way distributed sequential pattern mining using fruit fly algorithm along with Hadoop and map reduce framework
Authors: V. Malsoru; A.R. Naseer; G. Narsimha
Addresses: Department of CSE, JITS, Karimnagar, India ' Department of CSE, INHA University in Tashkent (IUT), Uzbekistan ' Department of CSE, JNTUHCE, Sultanpur, India
Abstract: Data mining is an effective tool used to take out information from big data as it provides several benefits to conquer the restrictions in it. In this paper, we present an innovative procedure developed using an updown directed acyclic graph (UDDAG) with fruit fly optimisation algorithm (FFOA), which is based on sequential pattern (SP) mining algorithm. In this work, the distributed sequential model mining algorithm is used to diminish the scanning time and scalability and the transferred database is employed to optimise the memory storage. The proposed method is used to expand the sequences in both the ends (prefixes and suffixes) of the identified model thereby supplying the consequences in quicker model expansion resulting in fewer database projections when compared to conventional methods. Our proposed method is implemented in Hadoop distributed surroundings to resolve the scalability issues and executed on JAVA platform using big datasets with Hadoop and map reduce framework.
Keywords: data mining; updown directed acyclic graph; UDDAG; fruit fly algorithm; distributed sequential model mining; Hadoop with map reduce framework.
International Journal of Computer Aided Engineering and Technology, 2021 Vol.14 No.4, pp.503 - 519
Received: 04 Nov 2017
Accepted: 02 Aug 2018
Published online: 14 Apr 2021 *