Open AccessReduction of Frequent Itemsets Mining in Big Data with the Help of FP Algorithm and Msegt-Tree
Author(s) -
Srinivasa Rao Divvela*,
Dr V Sucharita
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.d1666.029420
Subject(s) - computer science , data mining , reduction (mathematics) , node (physics) , big data , data stream mining , tree (set theory) , search engine indexing , data stream , set (abstract data type) , process (computing) , root (linguistics) , algorithm , mathematics , artificial intelligence , engineering , mathematical analysis , telecommunications , linguistics , philosophy , geometry , structural engineering , programming language , operating system
Frequent itemset mining is very crucial to minimize the cost and time of executions but when considering multiple distributed data streams in big data the frequent itemset mining has been a little cost consuming and taking more space and time complexity. In this paper we reduce the load and minimize the cost while minimizing the space and time complexities of the process by using reduction mechanism and indexing structures for preserving complexities. A 2-level architecture modal which will be helpful in handling the distributed data streams where the root node will be in level-0 and local nodes at level-1 is proposed. Each local node will evaluate the patterns in their specific data stream using the algorithm ‘FP’ which will help in lessening the burden on the root node and will be sent to root. With help of the patterns received from local nodes the root will generate a global pattern set.