DISTRIBUTED FREQUENT PATTERN ANALYSIS IN BIG DATA









Abstract

Big data was created to deal with big and complicated datasets that are challenging to process using typical data-processing methods. Big data is currently being utilized to extract value from datasets through predictive analysis and other advanced data analytics. This paper introduces incremental Frequent Pattern (FP) analysis, a novel approach. The suggested incremental FP-Growth analysis is used to build a tree structure with the lowest redundancy, and the FP growth technique is used to locate frequent itemsets in a database without the need for candidate generation. This will reduce the number of scans in the database, lowering latency.


Modules


Algorithms


Software And Hardware